How Firms Respond to
Mandatory Information
Disclosure
Anil R. Doshi
Glen W.S. Dowell
Michael W. Toffel
Working Paper
12-001
Copyright © 2011 by Anil R. Doshi, Glen W.S. Dowell, and Michael W. Toffel
Working papers are in draft form. This working paper is distributed for purposes of comment and
discussion only. It may not be reproduced without permission of the copyright holder. Copies of working
papers are available from the author.
HOW FIRMS RESPOND TO MANDATORY INFORMATION DISCLOSURE§
Anil R. Doshi
Harvard Business School
Wyss Hall
Boston, MA 02163
[email protected]
Glen W.S. Dowell
Johnson School of Management
350 Sage Hall
Cornell University
Ithaca, NY 14853-6201
[email protected]
Michael W. Toffel
Harvard Business School
Morgan Hall 497
Boston, MA 02163
[email protected]
July 4, 2011
We explore which organizations are particularly likely to resist, or acquiesce to, new institutional
pressures that arise from mandatory information disclosure regulations. We hypothesize that when
information is disclosed about organizational performance, certain organizational characteristics
amplify pressures to improve. Examining organizational responses to a change in a prominent
information disclosure program, we provide some of the first empirical evidence characterizing
organizations’ heterogeneous responses to information disclosure regulations. We find that private
ownership and proximity to headquarters and corporate siblings are associated with superior
performance trends following information disclosure. We also find that regional density moderates
effect of establishment size on performance improvement. We find no evidence that capability
transfers are associated with performance improvement. We highlight implications for institutional
theory, managers, and policymakers.
Keywords: information disclosure, institutional theory, empirical analysis, Toxics Release
Inventory, environmental strategy, mandatory disclosure.
INTRODUCTION
Organizations are required to respond to a host of diverse, external pressures from varied
constituencies and stakeholders. Among these are regulators who impose market-based, mandatory
information disclosure programs. Outside stakeholders utilize information disclosed about firm behavior
to exert pressure to modify or curb undesired behaviors. Recent years have seen a significant increase in
the use of information disclosure as a regulatory mechanism. Information disclosure has, for example,
been used to force firms to reveal details of the use and disposal of toxic chemicals, require food
manufacturers to post nutritional information about their products, and force restaurants to reveal their
hygiene scores.
§
The authors thank Luca Berchicci, Andrew King, Michael Lenox, and Chris Marquis for insightful comments, and
gratefully acknowledge financial support from Harvard Business School’s Division of Research and Faculty
Development.
1
Most research on the effectiveness of information disclosure has focused on average effects
across the population of affected firms, on aspects of disclosure programs that influence their success
(Weil et al., 2006; Fung, Graham, and Weil, 2007), or on how variation in their external environment
affects firms’ responses (Delmas and Toffel, 2011). But the structure and nature of organizations may
account for differential responses to external pressure. Hence, our research questions: “When new
institutional pressures arise, which organizations are particularly likely to resist or acquiesce?” and
“When subjected to new information disclosure mandates, which types of organizations are more likely to
subsequently improve their performance in ways that meet policymakers’ objectives?”
In this paper, we argue that the degree to which performance improves following information
disclosure will depend, in part, on specific features of the organizations. We hypothesize that greater
improvement will be seen among establishments subject to greater internal and external pressure to
improve as well as among those with greater access to the capabilities needed to do so. We test our
hypotheses using data from one of the most famous instances of information disclosure legislation, the
Toxics Release Inventory (TRI), which is often credited with bringing about significant improvements in
environmental performance (Hart, 2010). We examine how thousands of organizations have responded to
this regulatory requirement to publicly disclose emissions of hundreds of toxic chemicals, and exploit an
exogenous shock that occurred when the agency expanded the number of chemicals required to be
reported. Our research is especially important given the prominence of the TRI program and the
importance of achieving improved environmental performance.
We examine the differential performance of establishments based on five organizational
moderators: proximity to headquarters; proximity to siblings; presence of better-performing siblings;
establishment size; and ownership structure, namely, whether the parent firm is public or private. Using
emissions reductions as our performance indicator, we find that establishments located close to their
headquarters outperform those with headquarters farther away. We also find that establishments with
proximate siblings outperform those with siblings that are not proximate, and that in dense regions larger
establishments outperform smaller establishments, but in sparse regions no effect of size is observed.
2
Finally, establishments owned by privately held firms outperform establishments owned by publicly
traded firms.
The remainder of our paper proceeds as follows. In the next section, we review the pertinent
literature on institutional theory and information disclosure responses. We then develop theory and
hypotheses that relate organizational characteristics to responses to this new institutional pressure.
Detailed descriptions of our data and the methods used to test our hypotheses follow. We conclude with a
discussion of our results and their implications.
LITERATURE REVIEW
Two related streams of literature form the foundation for the theory on which we base our
hypotheses. We first consider research that examines how organizations respond to information
disclosure, with particular attention to the relatively small set of papers that analyzes firms’
heterogeneous reactions to information disclosure requirements. We then briefly review the relevant
literature from neo-institutional theory, the broader theoretical field most relevant to understanding how
information disclosure requirements affect organizations.
Responses to information disclosure
One stream within the literature on how information disclosure affects organizations is focused
on how stakeholders including journalists, investors, and customers respond to information disclosed
about a firm. For example, prior studies have found that information disclosed about firms’ pollution
levels can stimulate media coverage and depress market valuations of the firms (Hamilton, 1995) as well
as of neighboring homes (Oberholzer-Gee and Mitsunari, 2006). Other studies have found that investors
enjoy excess returns following increased mandatory corporate financial disclosures, because such
disclosure forces managers to heighten their focus on shareholder value(Greenstone, Oyer, and VissingJorgensen, 2006), and that disclosure of nutrition information can lead customers to reduce their caloric
3
consumption (Bollinger, Leslie, and Sorensen, 2011). Still other studies have found regulators to reduce
scrutiny of companies that self-disclose compliance violations (Toffel and Short, 2011).
Whereas most of these studies have examined average treatment effects, a few recent studies have
identified heterogeneous effects of information disclosure. For example, disclosure of nutritional
information was found to be particularly effective in reducing caloric consumption in areas populated by
wealthier, more highly educated consumers who had previously consumed more calories (Bollinger et al.,
2011). Information disclosure programs may also interact with other regulatory mechanisms.
For
example, facilities have been found to improve to a greater degree following disclosure of environmental
information in states in which they are also required to evaluate and report on their production processes
(Bennear, 2007). More closely related to our research are studies that have investigated organizations’
responses to information disclosed about them. For example, two studies that examined how graduate and
professional schools responded to school rankings found that officials in lower ranked schools reacted
defensively by focusing on their respective strengths and reallocating resources strategically to influence
future rankings (Elsbach and Kramer, 1996; Espeland and Sauder, 2007). Several studies have concluded
that government programs requiring disclosure of performance information have spurred companies to
improve environmental performance (Blackman, Afsah, and Ratunanda, 2004; Konar and Cohen, 1997;
Scorse, 2010), food and water safety (Bennear and Olmstead, 2008; Jin and Leslie, 2003), and surgical
outcomes (Cutler, Huckman, and Landrum, 2004; Hannan et al., 1994; Peterson et al., 1998).
A few studies have identified factors associated with particularly acute organizational responses
to information disclosure. For example, studies have found performance especially likely to improve in
organizations that receive significantly poor ratings (Blackman et al., 2004; Chatterji and Toffel, 2010;
Scorse, 2010), particularly in the face of the additional external pressure of being in a highly regulated
industry (Chatterji and Toffel, 2010). Other studies have found greater improvement among franchised
establishments revealed by an information disclosure program to be performing substantially below
company-owned counterparts (Jin and Leslie, 2009).
4
Mandatory information disclosure policies are premised on the notion that forcing organizations
to reveal information can induce stakeholder pressure which prompts the organizations to change their
behavior (Weil et al., 2006). Such pressure, however, is not uniformly distributed. Prior literature has
shown pressure to vary with location, industry, and the nature of the information disclosed. For example,
environmental concerns are more salient in some locations than in others (McConnell and Schwab, 1990;
Sine and Lee, 2009). Regional differences are also observed in relation to health concerns and enactment
of health-related policies. For example, tobacco control policies in universities are more prevalent in some
regions of the United States than in others (Halperin and Rigotti, 2003). Another geographic dimension
associated with differential responses is proximity to competitors. For example, changes in consumer
buying behavior are more likely to be observed in markets subject to competition, which facilitates
customers’ switching when newly available information reveals existing suppliers to be worse-performing
(Bollinger et al., 2011).
Institutional theory
Institutional theory provides a basis for studying how external pressures affect organizational
behavior. The rationale for mandatory information disclosure programs is to provide better information to
firms’ stakeholders including customers, investors, employees, government agencies, and interest groups.
For example, restaurant patrons’ consumption of high calorie foods may decline following restaurants’
disclosure of nutritional information, and local groups may protest pollution levels at a facility following
publication of its Toxic Release Inventory results. Such actions constitute a form of institutional pressure
that can motivate some firms to improve along the metrics of the information disclosed.
Much of the empirical research that examines the effects of institutional pressure has focused on
how organizational practices diffuse through an organizational field (e.g., Delmas and Toffel, 2008;
Edelman, 1992; Tolbert and Zucker, 1983) or on the degree to which organizational compliance with
institutional demands is merely symbolic, involving no substantive changes to operations (Goodrick and
Salancik, 1996; Okhmatovskiy and David, 2011; Toffel and Short, 2011; Westphal and Zajac, 2001). In
5
contrast, we examine not how pressures induce firms to adopt specific practices, real or symbolic, but
rather how institutional pressures lead to heterogeneity in changes in organizational performance.
Our work also relates to studies of firms’ reactions to institutional pressures exerted by regulators
(Henriques and Sadorsky, 1996; Khanna and Anton, 2002; Reid and Toffel, 2009), local communities
(Florida and Davison, 2001; Henriques and Sadorsky, 1996), customers (Delmas and Montiel, 2008),
competitors (Darnall, 2009), and shareholders (Reid and Toffel, 2009). But whereas these studies, like
most in the institutional theory domain, tend to focus on average organizational responses to pressures
emanating from different sources, our work examines heterogeneous responses. A recent review of
empirical literature based on institutional theory concludes that there exists a “lack of understanding of
the conditions under which institutional pressures and organizational characteristics explain the adoption
of beyond compliance strategies” (Delmas and Toffel, 2011). Recent studies have found evidence that
organizational responses to institutional pressures are moderated by organizational structure (Delmas and
Toffel, 2008; Okhmatovskiy and David, 2011), location (Lounsbury 2007), marginal operating costs, and
perceived benefits (Chatterji and Toffel, 2010). By theorizing and empirically testing hypotheses that
certain organizational factors moderate how organizations respond to institutional pressure, we contribute
to the nascent literature that examines heterogeneous responses to institutional pressures.
We build on the prior literature, which largely focuses on external institutional factors associated
with differential responses to information disclosure, by theorizing that particular organizational attributes
also affect the degree to which organizations alter their performance in response to information disclosure
mandates. Some of the limited research in this domain has found that firms facing lower-cost
opportunities to improve were especially likely to do so after being rated poorly by a third-party (Chatterji
and Toffel, 2010). Although a cross-sectional analysis found an association between family ownership
and lower pollution levels (Berrone et al., 2010), our study is, to our knowledge, among the first to
explore how organizational attributes moderate firms’ responses to institutional pressures that stem from
mandatory information disclosure. We propose that three organizational attributes, in particular, moderate
companies’ responses to local environmental pressures associated with disclosure of pollution levels.
6
Specifically, we hypothesize that privately owned and more geographically concentrated establishments
will be particularly responsive to these pressures by exhibiting greater environmental performance
improvement, and that the effect of establishment size on improvement will depend upon local
competitive conditions.
THEORY AND HYPOTHESES
Information disclosure, pressure, and performance
The effectiveness of information disclosure in changing firms’ (and individuals’) behaviors
hinges on the perceived costs and benefits of the changes (Chatterji and Toffel, 2010; Fung et al. 2007;
Jin and Leslie, 2009). For example, Jin and Leslie (2009) model restaurants’ hygiene changes as a
function of the marginal cost of improvement and the benefit of increased business that stems from
improved hygiene. Chatterji and Toffel (2010) argue that firms in environmentally sensitive industries
incur higher costs from disclosure of poor environmental performance due to the increased potential for
inspection and greater intensity of public scrutiny. They find firms in such industries to be more likely to
improve performance after receiving poor environmental ratings. This illustrates a common issue with
information disclosure as intended to “reduce specific risks or performance problems” (Fung et al., 2007:
5). That is, information disclosure, whether mandated by government or promulgated by private parties
such as ratings agencies, is intended to shine a light on previously hidden dimensions of performance with
the intention of spurring improvement on the part of the disclosing actors.
We suggest that information disclosure is more likely to lead to improved performance among
establishments that attract particularly salient pressures from internal or external stakeholders and that
have preferential access to intra-organizational expertise. The degree to which concern about internal and
external pressures prompts managerial responses depends on particular characteristics of an establishment
and its parent company. We hypothesize below that proximity to headquarters motivates improved
performance and provides the means to achieve it. We also hypothesize that certain organizational
7
characteristics that heighten the perception of external pressure following information disclosure
mandates account for firms being particularly likely to respond by improving their performance.
The role of internal pressure and ease of capability transfer in performance improvement
Information that reveals an establishment like a restaurant (Jin and Leslie, 2009), factory (King
and Lenox, 2002), or vehicle service station (Pierce and Toffel, 2011) to be performing poorly can affect
the reputation of its parent and sibling organizations. Because poor performance revealed by information
disclosure requirements can harm organizations’ reputations and stock prices (Hamilton, 1995; Konar and
Cohen, 1997), the revelation of poor performance can prompt investment in improved procedures
(including staff training and internal monitoring) and capital equipment aimed at improving performance
(Chatterji and Toffel, 2010). We consider how revealing its poor performance generates pressure on an
establishment to improve, and how that pressure is magnified by an establishment’s embeddeness in a
community.
Local embeddedness
When poor performance is revealed by mandatory information disclosure, pressure from an
establishment’s stakeholders can prompt subsequent performance improvement. Because firms tend to be
particularly embedded in the communities in which they are headquartered (Marquis and Battilana, 2009),
they behave in ways that seek to preserve their relationships with those communities. This embeddedness
magnifies the effect of stakeholder pressure on subsidiary establishments located in their headquarters
communities. For example, such establishments, being especially likely to try to ingratiate themselves to
their communities, are less likely to lay off workers (Greenwood et al., 2010) and more likely to source
from local firms (Audia and Rider, 2010). Similarly, we argue that establishments located in their
headquarters’ communities will be particularly responsive to stakeholder pressures.
Proximity to headquarters also magnifies internal pressure on establishments revealed to be
performing poorly because the information is particularly visible and salient to top management, which
has strong incentives and the authority to press for improved performance. Proximity also facilitates the
8
monitoring of and provision of assistance to such establishments. To summarize, proximity to
headquarters is likely to magnify pressure from external and internal stakeholders, which increases the
intensity with which the establishment will respond.
H1: Environmental performance will improve to a greater degree in establishments in the
same geographic area as their headquarters than in establishments outside their
headquarters area.
Proximity to sibling establishments
Proximity not only to headquarters, but also to other establishments owned by the same parent
can increase pressure on poorly performing establishments to improve. Being collocated with sibling
establishments increases an establishment’s visibility in a local area and ties siblings together such that
information about one establishment, whether positive or negative, can affect the others (Carney and
Gedajlovic, 1991). This reputational spillover means that any given establishment’s performance has
implications for sibling establishments in the same area (Jin and Leslie, 2009). Thus, poorly performing
establishments with proximate siblings can experience strong internal pressure to improve.
In addition to fostering reputational spillovers that increase internal pressure, proximity to
siblings facilitates the monitoring of, and transfer of capabilities needed to improve, performance. The
personal, face-to-face communication that facilitates knowledge transfer (Darr, Argote, and Epple, 1995)
becomes more costly and difficult as distance increases (Lafontaine and Slade, 2007; Berchicci, Dowell,
and King, 2011). Indeed, one prescription for facilitating knowledge transfer is to cultivate closer
relationships between sender and receiver (Szulanski 1996). To the extent that it engenders such
relationships, physical proximity will enhance an establishment’s ability to learn from its siblings.
H2: Environmental performance will improve to a greater degree in establishments with
proximate corporate siblings than in isolated establishments.
Capability transfer
We hypothesize above two mechanisms that lead to improvement, proximity enhancing both
reputational spillovers and the ease with which capabilities are transferred. Although we expect both
9
mechanisms to play a role in spurring improvement, it would be useful to isolate their effects in order to
assess their relative magnitudes. We outline conditions under which capability transfer is most likely, in
order to assess the degree to which that mechanism leads to greater performance improvement. We argue
that two conditions are necessary for an establishment to benefit from capability transfer from a sibling:
(1) the presence of a superior-performing sibling, and (2) a perceived need to enact the transfer.
The first necessary condition for capability transfer is the existence of the capability in at least
one of an establishment’s siblings. An establishment that has no other siblings that possess the capability
is left to develop it on its own or attempt to learn other firms, which is more difficult because capabilities
and the tacit knowledge on which they depend transfer more readily within than across firms (Darr, et al.
1995). Thus, an establishment with at least one superior performing sibling is more likely to benefit from
capability transfer than an establishment that lacks such a repository of capability.
The existence of a superior performing sibling that possesses the potential to provide capabilities
does not guarantee the transfer of those capabilities to a poorly performing sibling. Significant barriers to
transferring capabilities within organizations often results in persistent performance differences between a
firm’s establishments (Chew, Bresnahan, and Clark, 1990; O’Dell and Grayson, 1998; Szulanski, 1996).
Absent an event that creates urgency, these barriers can impede the transfer of capabilities to where they
are needed most (Berchicci et al., 2011). Disclosure of information about an establishment’s performance
can generate the sense of urgency that spurs efforts to overcome these barriers. Thus, to the extent that
intra-organizational capability transfers are activated following information disclosure, greater
performance improvement will be observed among establishments that have stronger performing siblings.
H3: Environmental performance will improve to a greater degree in establishments with
stronger-performing corporate siblings than in establishments with only weakerperforming corporate siblings.
Proximity enhancing capability transfer
Capability transfers, even when prompted by a sense of urgency, can nevertheless be difficult to
achieve due, for example, to the complexity of the capability to be transferred, a poor relationship
10
between sender and receiver, and lack of absorptive capacity on the part of the recipient (Szulanski 1996).
One factor that can diminish the barriers to knowledge transfer is the proximity between the source and
recipient. Proximity is particularly beneficial for overcoming the barriers associated with complexity of
the knowledge and for improving the relationship between the source and recipient. Proximity reduces the
cost of context-rich interactions, such as face-to-face meetings (Argote, McEvily, and Reagans, 2003) that
are particularly beneficial for transmitting tacit knowledge that underlies capabilities, and increases trust
between the sender and recipient (Bresman, Birkinshaw, and Nobel, 1999; Szulanski, Cappetta, and
Jensen, 2004). Establishments proximate to each other share a common institutional context, which also
increases the likelihood that information conveyed between them is relevant and likely to be accepted
(Williams, 2007). Therefore, when establishments have well performing siblings from which they can
learn, proximity to these siblings can enhance the transfer of their superior capabilities (Berchicci et al.,
2011; Darr et al., 1995).
H4a: Environmental performance will improve to a greater degree in establishments that
are proximate to well-performing corporate siblings.
Company headquarters can be a repository of capabilities (Grant, 1996) and disseminator of the
best practices exhibited by its subsidiaries (Lenox and King, 2004). Establishments proximate to their
headquarters are especially likely to be exposed to such best practices because proximity facilitates the
interpersonal interaction through which tacit knowledge is often disseminated. Proximity to headquarters
also enhances monitoring (Kalnins and Lafontaine, 2010) which allows the headquarters to more
effectively assess how successfully the capabilities are transferring.
H4b: Among establishments with well-performing corporate siblings, environmental
performance will improve to a greater degree in those proximate to headquarters.
The role of external pressure in performance improvement
Organizational characteristics affect not only internal (intra-firm) pressures, but also the salience
of external pressures. We hypothesize that, among establishments that face common institutional
pressures, size and ownership structure will moderate the responses to performance disclosure. Below, we
11
theorize that relative size within its institutional field affects the pressure on, and thus response of, an
establishment. We further propose that establishments owned by publicly traded firms face different
pressures than those owned by private firms, which leads to different responses. For each of these
organizational characteristics, we offer competing theories that predict opposing moderating effects.
Establishment size and regional density
Although theory suggests that size is likely to moderate an establishment’s sensitivity to external
pressures, whether size promotes or inhibits such sensitivity remains ambiguous. Institutional theorists,
for example, argue that larger organizations’ greater visibility makes them especially attendant to the need
to maintain legitimacy (Goodstein, 1994; Ingram and Simons, 1995), which suggests that they would be
particularly sensitive to external pressures occasioned by information disclosure. Resource dependence
theory (Pfeffer and Salancik, 1978), on the other hand, suggests that firms will attend to pressures that
emanate from stakeholders who control critical resources. To the extent that larger firms are more
powerful and thus less dependent on local regulators and pressure groups, they can be expected to be
better able to resist external pressures that surface in the wake of disclosure of performance information
(Drope and Hansen, 2006; Grant, Bergesen, and Jones, 2002).
We propose that these opposing views can be reconciled by considering how an organization’s
context influences the mechanisms by which size affects sensitivity to pressure. Institutional theorists’
argument that maintaining legitimacy is more important for larger firms (Goodstein, 1994) is based on the
assumption that larger organizations, being more visible in their communities, are more likely to attract
media attention (Ingram and Simons, 1995) and be held to higher standards than smaller counterpart
organizations (Goodstein, 1994).
But despite being more visible and under greater scrutiny, larger establishments may also be
better positioned to resist, and therefore less sensitive to, local pressure generated by information
disclosure. Larger establishments can accrue power through superior political access, and can more easily
afford to lobby or donate to politicians and to sue regulatory agencies (Drope and Hansen, 2006; Hansen,
12
Mitchell, and Drope, 2005; Schuler, 1996). Greater political and social capital may also accrue to larger
establishments by virtue of providing employment for greater numbers of individuals. Larger
establishments can direct these formidable resources at reducing the pressure exerted on them by local
regulators or non-governmental organizations to improve performance revealed by mandatory
information disclosure. Thus, larger establishments may exhibit less improvement than smaller
establishments in the wake of disclosure of performance information.
The different theories articulated above thus suggest opposing influences of size: larger
organizations might be either particularly responsive, or particularly unresponsive, depending on which
theory is embraced, to external pressures that follow information disclosure. Under what circumstances
might each mechanism dominate? We suggest that a critical factor that moderates how size affects an
organization’s responsiveness is its economic, social, and political power in the region from which the
pressure emanates. For example, a 1,000-employee establishment that accounts for but a tiny fraction of
economic activity in a region has less power than a similar-size establishment that accounts for a large
proportion of the economic activity in its region. A large number of establishments in a given area dilutes
any given establishment’s potential power, rendering it more vulnerable to pressure exerted by regulators
and concerned groups. A large establishment will have much greater leverage in the presence of relatively
few other establishments. Such an establishment will likely be able to leverage its contributions to
community tax revenues and employment (e.g., Boal and Ransom, 1997) or membership in the local elite
and influence with local regulators (Marquis and Battalina, 2009).
The foregoing arguments suggest that the number of other establishments in a region moderates
the effect of establishment size on performance improvement following information disclosure in the
following way. We consider a region with a relatively large number of reporting establishments to be a
dense region, and one with relatively few reporting establishments to be a sparse region. Stated as a pair
of difference-in-difference relationships, we expect larger establishments to exhibit (a) greater
performance improvement than smaller establishments in dense regions, but (b) less improvement than
smaller establishments in sparse regions. We combine these relationships into a single statement by
13
hypothesizing a triple difference: that relative improvement among large establishments will be greater in
dense regions than in sparse regions.1
H5: Larger organizations’ environmental performance improvement will exceed that of
smaller establishments to a greater degree in dense regions than in sparse regions.
Public ownership
Following disclosure of performance information, there are several reasons why performance
improvement can be expected to be greater among establishments owned by publicly traded firms than
among establishments owned by privately held firms. First, being accountable to a greater number of
audiences, publicly traded firms face more requirements than their privately owned counterparts to report
information about their operations (Fischer and Pollock, 2004; Mascarenhas, 1989). Second, publicly
traded firms are vulnerable to investors who seek to use their ownership stake to influence management
decisions. Whereas public firms have investors that have access to publicity-laden shareholder resolutions
and other mechanisms through which they can attempt to influence management decisions, private firms
seldom encounter activist investors (David, Bloom, and Hillman, 2007; Gillan and Starks, 2000; Reid and
Toffel, 2009). Third, disclosed information can affect the stock price of publicly traded firms (Konar and
Cohen, 1997), which can lead corporate managers to increase pressure on subsidiary establishments to
improve performance. The absence of stock price concerns renders private firms less sensitive to external
pressure to improve following disclosure of performance information (Schulze et al., 2001).
H6a: Environmental performance will improve to a greater degree in establishments owned
by publicly traded firms than in establishments owned by privately held firms.
Conversely, that public firms’ shareholders are often principally concerned with share price may,
in fact, reduce public firms’ sensitivity to mandatory disclosure of performance information. Operational
improvements (e.g., capital equipment, development of new capabilities) often require significant
additional investment that have long payback periods, and sometimes are not profitable even in the long
1
Foreshadowing our empirical approach, we analyze each of these two difference-in-differences, which compare
performance trends of large establishments to small establishments in sparse regions, and separately in dense
regions, and then compare the trend differences estimated by the two regressions.
14
run (Christmann, 2000; King and Lenox, 2002). To the extent that financial returns derived from
investments in operational performance improvements are unclear or occur only over long time horizons,
we expect publicly traded firms under pressure to maintain short term profits and stock prices to be less
likely to make such investments (Fischer and Pollock, 2004). Private firm owners are relatively more
insulated from short term financial pressures exerted by outside investors and likely to have significant
portions of their own wealth concentrated in their firms (Moskowitz and Vissing-Jørgensen, 2002), both
of which increases the likelihood that they will make long-term investments to secure the firms’ survival
(Schulze et al., 2001).
H6b: Environmental performance will improve to a greater degree in establishments owned
by privately held firms than in establishments owned by publicly traded firms.
DATA AND MEASURES
Empirical context and sample
We empirically test our hypotheses by taking advantage of a policy change that occurred when
the U.S. Environmental Protection Agency (EPA) expanded the scope of the Toxics Release Inventory
(TRI). The U.S. Emergency Planning and Community Right-to-Know Act of 1986 created the TRI, which
requires establishments to publicly report annually waste, transfers, and releases of certain toxic
chemicals. An establishment is required to report if it (1) operates within particular industry sectors
including manufacturing, mining, electric utilities, hazardous waste treatment, and chemical distribution,
(2) employs ten or more individuals, and (3) manufactures, imports, processes, or otherwise uses any of
the listed toxic chemicals in amounts that exceed reporting thresholds (U.S. EPA 2004). TRI became
operational in 1987, and EPA has periodically expanded the list of chemicals to be reported. We leverage
this fact in our identification strategy, as described below. As of 2011, the EPA requires disclosure of 593
individual chemicals in 30 chemical categories (U.S. EPA, 2011). To construct our database, we
supplement establishments’ annual TRI reports with Dun and Bradstreet data obtained from the National
Establishment Time-Series (NETS) database, as described below. Our resulting panel dataset consists of
15
38,175 establishments over the years 1995 to 2000 (217,575 establishment-years), the six-year period that
followed EPA’s largest expansion of the list of chemicals that comprises the TRI.
Dependent variable
We measure environmental performance based on toxic chemical emissions data from the TRI
database, a widely used approach (e.g., Berrone et al., 2010; Chatterji and Toffel, 2010; King and Lenox,
2000; King and Shaver, 2001; Toffel and Marshall, 2004). In November 1994, the EPA issued a rule that
expanded the list of toxic chemicals required to be reported by 243 (in addition to the 363 already
required to be reported), effective in 1995 (U.S. EPA, 1994). Our outcome measure is total releases of
these 243 chemicals that were added in 1995, which includes the total pounds each firm reported to the
TRI as production waste, transfers, and emissions. We obtained TRI data from the EPA’s Risk-Screening
Environmental Indicators (RSEI) Model (versions 2.1.2 and 2.1.3) (U.S. EPA, 2010). Our models employ
the log of these annual values after adding one. Whereas some studies apply various weights to these
chemicals to account for differences in toxicity, simply summing the pounds of emissions was a
commonly used approach by the media and prominent nonprofit organizations and in government
publications during the sample period (Toffel and Marshall, 2004), as well as by academic studies
examining institutional pressure and responses to TRI releases (e.g., Chatterji and Toffel, 2010, Dooley
and Fryxell, 1999, Feldman, Soyka, and Ameer, 1997, Konar and Cohen, 2001).
Moderators
Headquarters proximity
We measure proximity to headquarters as a dichotomous variable, proximate headquarters, coded
“1” for establishments located in the same city as their headquarters, and “0” otherwise. We obtained
establishment addresses from the TRI database and headquarters addresses from the NETS database. To
cleanly identify the effect of the 1995 policy change on firm behavior, we pursue the customary practice
of measuring the hypothesized establishment-level characteristics fixed at their value in 1994, just prior to
16
the policy change. In the absence of a 1994 value, we use an establishment’s 1993 value. We employ this
practice for all hypothesized moderators described below.
Sibling proximity
We measure the extent to which an establishment’s poor performance might impugn the
reputation of other establishments in its corporate family based on whether any other TRI-reporting
establishments are located within the same city. We created proximate sibling as a dichotomous,
establishment-level, time invariant variable coded “1” for establishments with at least one sibling in the
same city in 1994, and “0” otherwise. We obtained the identities and addresses of each establishment’s
siblings from the NETS database.
Progressive sibling
We created a dichotomous variable to indicate whether any of its siblings performed substantially
better than the focal establishment. We coded an establishment as possessing a progressive sibling as “1”
if at least one sibling had standardized abnormal emissions (in 1994) at least one standard deviation better
than (below) the focal establishment’s standardized abnormal emissions, and “0” otherwise. Based on an
approach pioneered by King and Lenox (2000), our calculation for standardized abnormal emissions
begins with an OLS regression that predicts total toxic releases based on employment, employmentsquared, and a full set of dummies for two-digit SIC codes. We include all chemicals that were part of the
TRI from its inception through 1994 (thereby excluding the chemicals added in 1995 that make up total
releases, our dependent variable). Employment and two-digit SIC codes are obtained from NETS. The
regression yields a predicted emissions level given an establishment’s size and industry. We calculate
abnormal emissions as an establishment’s actual emissions less its predicted emissions, which we then
standardize with a mean of zero and standard deviation of one within each two-digit SIC code.
Large establishment
We considered establishments to be large based on their employment levels in 1994 relative to
other TRI establishments in the same state. Specifically, we created large establishment as a time17
invariant, establishment-level, dichotomous variable coded “1” if an establishment’s employment in 1994
exceeded the median employment of all TRI establishments in the same state that year, and “0” otherwise.
We obtained establishment employment levels from NETS.
Regional density
We distinguish sparse from dense regions based on the density of TRI-reporting establishments in
a city. Specifically, we identify as sparse a city that had fewer TRI-reporting establishments during 1994
than the median number of 11 for all U.S. cities during those years. An establishment was considered to
be located in a dense city if the number of TRI-reporting establishments therein was at or above this
median number of 11.
Public ownership
We created a time-invariant, establishment-level, dichotomous variable, public ownership, coded
“1” if an establishment was owned by a publicly traded firm, and “0” if by a privately owned firm, in
1994, based on data from the NETS database.
Controls
An establishment’s toxic chemical emissions being, in part, a function of its activity level and
industry, we include several control variables to capture these important covariates.
Establishment size
We control for establishment size via employment level (Aravind and Christmann, 2011;
Chatterji and Toffel, 2010; Diestre and Rajagopalan, 2011; King and Lenox, 2000; King, Lenox, and
Terlaak, 2005; Potoski and Prakash, 2005; Russo and Harrison, 2005). We obtained annual employment
data from NETS and used log employment in our models to reduce skew.
Establishment production
We control for changes in production volumes by obtaining annual production ratios (i.e., the
ratio of an establishment’s production level in a given year to its production level the prior year) from the
18
TRI database (Berrone and Gomez-Mejia, 2009; Scorse, 2010; Terlaak and King, 2006). Establishments
are required to provide a production ratio for each chemical reported to the TRI database, each year. For
establishments that reported different production ratios in a given year (e.g., for different production lines
in a plant), we employed the median value for the establishment-year. Across the distribution of median
annual establishment production ratios, we winsorized at the 5th and 95th percentiles to avoid undue
influence from outliers. We then linearly interpolated missing interior production ratio values. Using
these production ratios, we calculated the relative production level for each establishment-year relative to
1994 (our baseline year) using the following equation. First, we created a normalized baseline year for all
establishments by setting relative production leveli,t equal to “1” in the year 1994. Then, for each
subsequent year starting in 1995, we calculated relative production leveli,t for establishment i in year t as
follows:
relative production leveli,t = production ratioi,t × relative production leveli,t-1
In our regressions, we include the log of relative production level to match our log dependent variable and
a dummy variable coded “1” if an observation’s value was based on an interpolated production ratio, and
“0” otherwise.
Industry
Toxic chemical emissions being, in part, a function of industry activities (Berrone et al., 2010;
Diestre and Rajagopalan, 2011; King and Lenox, 2000; Potoski and Prakash, 2005), we control for
differences between industries by including a full set of industry dummies based on 2-digit Standard
Industrial Classification (SIC) codes using NETS data. To facilitate model convergence, we collapsed
relatively rare SIC codes, that is, those with fewer than 100 establishment-year observations in each of
our samples, into a single “other” category.
Sample descriptive statistics and summary statistics and correlations are reported in Tables 1 and
2.
***************************
Insert Tables 1 and 2 about here
19
***************************
EMPIRICAL ANALYSIS
Model specification
Establishments are required to report to EPA only toxic chemical emissions that exceed specific
TRI reporting thresholds. As a result, our dependent variable, total releases, is missing (left-censored) in
years during which an establishment’s emissions range from zero (no emissions) to emissions levels that
fall below TRI reporting thresholds. Prior research indicates that establishments for which emissions dip
below reporting thresholds seldom cease entirely to use these chemicals, which suggests that they often
continue to release unreported emissions, and that treating such missing values as zero would likely result
in biased estimates (Bennear, 2008). As a result, we estimate an interval regression whereby left-censored
observations of the dependent variable are specified to range between 0 and the minimum positive value
of total releases reported by other establishments that year,
. We estimate the following model for
establishment i in year t:
,
,
where
,
,
,
is the latent dependent variable and is related to the observed dependent variable,
,
, based on
the following observation rule:
,
,
,
0
,
represents the time-invariant moderator described above,
in 1996, etc.) that captures the secular trend, and
,
is an annual counter (0 in 1995, 1
represents control variables (log employment, log
relative production level, production level interpolated, and industry dummies).
Identification
Our identification strategy relies on the exogenous policy shock that occurred in 1995 when EPA
expanded the number of chemicals required to be reported to the TRI substantially, by 243, from 363 to
606 chemicals. Our analysis compares how various types of establishments responded to the requirement
20
to disclose the newly listed chemicals. Specifically, we compare performance trends during a six-year
period that begins in 1995, the initial year the newly added chemicals were required to be reported, and
ends in 2000.
A number of our hypotheses predict behaviors based on the relationship between a focal
establishment and its siblings (H2-H4). To sharpen the identification, our empirical analysis of these
hypotheses is based on a sample restricted to establishments with at least one TRI-reporting sibling. This
enables us, when testing H2, to compare the behavior of establishments with proximate siblings to that of
establishments with non-proximate siblings, and, when testing H3, to compare performance trends
between (a) establishments with intra-firm access to knowledge and capabilities from better-performing
siblings, and (b) establishments with siblings that do not outperform them.
To estimate how density moderates the behavior of large organizations (Hypothesis 5), we test
whether the extent to which large establishments improved more than small establishments in sparse
regions is exceeded by the extent to which large establishments improved more than small establishments
in dense regions. We test this pair of difference-in-differences using a triple difference approach (i.e.,
differences-in-differences-in-differences), which has been used in many domains to facilitate comparisons
across two groups in two different contexts (e.g., Basker and Noel, 2009; Costa and Kahn, 2000; Currie et
al., 2009; Gruber, 1994). Specifically, we estimate our model on the subsample of establishments in cities
with few TRI-reporting establishments (sparse), and then on the subsample of establishments in cities
with many TRI-reporting establishments (dense). We then compare the coefficients that estimate the gap
in trends, and test whether the former is exceeded by the latter.
Primary results
We estimate our models using interval regression, and include each interaction term in a separate
regression model. In all cases, we report standard errors clustered by establishment, and include dummy
variables coded “1” if relative production level or log employment was recoded from missing to zero, and
“0” otherwise (Greene, 2008: 62; Maddala, 1977: 202;). This approach, commonly employed in
21
econometric analysis, is algebraically equivalent to recoding missing values with the variable’s mean
(Greene, 2007: 62). Our primary results are reported in Table 3.
************************
Insert Table 3 about here
************************
Headquarters proximity
The model displayed in Column 1 includes the interaction between the secular trend and
proximate headquarters. The results indicate that establishments in the same city as their headquarters
exhibited a superior environmental performance trend compared to establishments with more distant
headquarters (β = -0.040, p < 0.01), which supports Hypothesis 1. This interaction term coefficient
represents the average annual difference in emissions trends between these two groups of establishments.
To interpret the magnitude of this effect, we note the positive annual trend among establishments with
headquarters in a different city (the baseline group) (β = 0.088, p < 0.01), which equates to an average
increase of 0.53 log points over our six-year sample period (1995-2000) (calculated as 0.088*6), and an
increase of 59% beyond the total releases sample mean of 0.90 log points (Table 2). In contrast, the
average headquarters-proximate establishment increased total releases by just 0.29 log points (calculated
as [0.088-0.040]*6), which amounts to 32% of the sample mean. Given that the latter constitutes nearly
half the growth rate of the former, we conclude that this statistically significant difference is also a
substantial one.
Reputation spillover
To analyze the effects of a proximate sibling, we restrict the sample to establishments with at
least one TRI-reporting corporate sibling, and control for the number of siblings (fixed at their 1994
values, then logged). The results in Column 2 reveal that establishments with proximate siblings exhibit a
superior performance trend compared to establishments with non-proximate siblings (β = -0.020, p <
0.05), which supports Hypothesis 2. The average annual trend among establishments with non-proximate
siblings (β = 0.094, p < 0.01) implies a total 0.56 log point increase over the six-year sample period. In
22
comparison, establishments with proximate siblings increased total releases by 0.44 log points, a 21%
lower growth rate.
Capabilities transfer
The model that tests H3 compares the performance trends of establishments with superiorperforming corporate siblings to those of establishments with more poorly performing siblings, and is
estimated on the subsample of establishments with corporate siblings. The results (Column 3) indicate
that the performance trends of the two groups are statistically indistinguishable, which fails to support
Hypothesis 3.2
Proximity enhancing capability transfer
We next investigated whether capability transfers from superior-performing siblings are enhanced
by geographic proximity. To examine whether proximity enhances transfers made directly between
siblings, we analyze the subsample of establishments with progressive siblings and test whether the
performance of establishments proximate to their progressive siblings is superior to that of establishments
with progressive siblings further removed. The results reported in Column 4 provide no evidence of this.
To examine whether a focal establishment’s proximity to headquarters (which can serve as an
information clearinghouse) enhances capability transfers from a progressive sibling, we limit our sample
to establishments with at least one sibling. We find no evidence that establishments with a progressive
sibling and proximate headquarters outperform other establishments (Column 5). Thus, we find no
evidence to support Hypotheses 4a or 4b.
2
We also estimated this model on a subsample of establishments with siblings that likely faced greater pressure and
thus demand to access the intra-firm knowledge and capabilities of their progressive siblings. The subsample
includes those establishments whose overall toxic chemical releases were among the top ten in their county in 1994.
This model yielded no significant differences in performance trends between establishments with progressive
siblings and establishments whose performance was more akin to their siblings. Thus, we found no support for
Hypothesis 3.
23
Large organizations and density
Among establishments in sparse regions (cities with fewer TRI-reporters than the sample
median), we find a greater increase in total releases among larger than smaller establishments (Column 6:
β = 0.028, p < 0.01). Among establishments in more dense regions (the opposite subsample: cities with
more TRI-reporters than the sample median), the negative coefficient implies that larger establishments
improve more than smaller establishments, but the difference is not statistically significant (Column 7: β
= -0.013, p = 0.14). To test whether larger establishments’ superior performance relative to that of smaller
establishments in dense regions exceeded their relative performance difference in sparse regions, we
estimated a seemingly unrelated regression that simultaneously estimated these two models. The results
revealed a statistically significant difference between these two interaction term coefficients of opposing
signs (2 = 11.63, p < 0.01), which supports Hypothesis 5.
What is the magnitude of these differences? In sparse regions, smaller establishments’ (the
baseline group) positive (worsening) performance trend (β = 0.034, p < 0.01) amounts to a 0.20 log point
increase in total releases over the six-year sample period. In contrast, larger establishments increased total
releases by 0.37 log points, which amounts to nearly twice (1.9 times) the increase exhibited by smaller
establishments. The difference between large and small establishments is similarly substantial in dense
regions. Annual growth among smaller establishments (β = 0.058, p < 0.01), the baseline in the
regression, amounts to 0.35 log points of total releases over six years. In contrast, larger establishments
experienced an average total increase of 0.27 log points, which amounts to just 77% of the increase
exhibited by smaller establishments. To graphically depict the difference in trends between larger and
smaller establishment in sparse and dense regions, we calculated the gap in trends as follows. We
calculated the average annual predicted value for larger establishments within sparse regions, and
deducted from this the corresponding figures for smaller establishments. The growing gap over time
between larger and smaller establishments is depicted as the rising, bold, solid line in Figure 1,
surrounded by dashed lines representing the 95% confidence interval. We repeated these calculations for
dense regions. The narrowing gap over time between larger and smaller establishments is depicted as the
24
declining, thin, solid line in Figure 1. The difference between these trends is statistically significant, as
indicated by the chi-squared test statistic described above.
***************************
Insert Figure 1 about here
***************************
Public ownership
As reported in Column 8, average environmental performance trends were better for privatelyheld establishments than for publicly-owned establishments (β = 0.060, p < 0.01), which supports
Hypothesis 6b, but not Hypothesis 6a. The average annual trend of publicly-owned establishments (0.035
+ 0.060 = 0.095) was nearly triple (2.7 times) the average annual trend of privately-owned establishments
(β = 0.035, p < 0.01). Over the six year sample period, these average trends amount to total emissions
increasing by 0.57 log points (63% of the sample mean) for establishments with publicly owned parent
firms, compared to 0.21 log points (23% of the sample mean) for establishments with privately held
parent firms.
Robustness tests
We conducted a number of robustness tests to assess the sensitivity of our results to controlling
for establishments’ pre-trends and local environmental pressures, moderator measurement, model
specification, and subsampling strategy.
Historical performance trends
Our primary analysis examines the environmental performance trends of toxic chemical
emissions in the first few years after public disclosure was required. We conclude from our primary
results that public disclosure influenced the differential subsequent performance trends we observed. A
potential threat to the validity of this conclusion is our inability to observe historical performance trends
for these chemical emissions. Were the performance trends that became publicly observable with the new
disclosure requirements perhaps already occurring beforehand, but known only to the establishments?
While we cannot observe that trend, we can observe establishments’ performance trends of the original
25
TRI chemicals whose reporting had been required before the 1995 policy change. We conducted the
following tests to assess the likelihood that the validity of our conclusions is threatened by these historical
performance trends. We calculated each establishment’s historical release trend based on other toxic
chemicals that were required to be reported from 1991 to 1994, the years immediately preceding the 1995
expansion. Specifically, for each establishment i, we calculated a percent change metric that is robust to
outliers using the following equation:
historical releases trendi = (average total releasesi,1993-1994 – average total releasesi,1991-1992) /
(0.5×average total releasesi,1993-1994 + 0.5×average total releases i,1991-1992)
By construction, this metric ranges from –2 to +2.
To control for the influence of historical performance trends (1991-1994) on establishments’
subsequent performance trends (1995 and after), we added to our model the interaction between historical
releases trend and the annual counter. Because historical releases trend was sometimes undefined, owing
to an establishment’s total releases throughout 1991-1994 being below the reporting threshold or actually
zero, we recoded missing values of historical releases trend to zero, and created a dummy variable to
indicate these instances of recoding. We included in the regression both this dummy variable and its
interaction with the annual counter. These models yielded results (reported in Table A-1 in the Appendix)
substantively the same as the primary results for all hypotheses.
Alternative specifications for independent variables
In testing our hypotheses, we had to choose an appropriate geographic scope to create our
measures of proximity for headquarters and siblings, as well as for assessing the effect of regional density
on the relative improvement of large and small establishments. To determine if our results were sensitive
to these decisions, we performed a number of robustness tests. For headquarters proximity, our results
are substantively similar when we measure proximity more narrowly, as the same three-digit ZIP Code, or
more broadly, as the same state (results reported in Columns 1-2 of Table A-5 in the Appendix). For
sibling proximity, the results are substantively similar when we measure proximity more narrowly, as the
26
same three-digit ZIP Code (Table A-5, Column 3). Relying on a broader geographic definition of
proximity (same county) also yields a negative coefficient, but one that is half the magnitude and not
statistically significant (Table A-5, Column 4: β = -0.012, p < 0.16). These results begin to sketch the
boundaries that limit the geographic scope of reputation spillovers.
In testing the moderating effect of local density on the relative improvement of large and small
facilities, we measured density at the city level and considered facilities to be large if they employed more
people than the median facility in their state. We continue to find support for Hypothesis 5 when we
categorize sparse versus dense communities based on the number of TRI establishments in the same
county being less or greater than the county median of 41 (Columns 1-2 of Table A-6 in the Appendix).3
We also continue to find support for Hypothesis 5 when we define large establishments as those with
employment levels among the top quartile in their state, rather than exceeding the state median, in 1994.4
In testing the effect of capability transfer, we assumed that an establishment could learn from any
of its siblings with superior capabilities, even if those siblings are in unrelated industries. It may be,
however, that similarity in production processes facilitates transfer. However, we continue to find no
evidence of capabilities transferred from progressive siblings when we focus on those that shared with the
focal establishment the same two-digit SIC code (Column 5 of Table A-5). We also assessed whether
changing the definition of progressive sibling would affect our results, but there was no affect for
facilities with siblings that exhibited dramatically better performance, measured as two standard
deviations lower abnormal releases (results not reported).
3
Larger establishments exhibited worse performance trends than smaller establishments in sparse counties, and
better performance trends than smaller establishments in dense counties.
4
In sparse cities, we continue to observe larger establishments exhibiting significantly worse performance trends
than smaller firms, using either of these definitions of large (Column 3 of Table A-6, p<0.01). In dense cities, we
find little difference in performance between large and small establishments using this alternative measure (Column
4). Seemingly unrelated regression indicated that the difference between these trends is statistically significant
(2=10.70, p<0.01).
27
Local environmental preferences
Because some prior studies have highlighted the importance of local community environmental
pressures on establishments’ environmental management practices (e.g., Delmas and Toffel 2008;
Hamilton, 1999), we assessed the extent to which our results are robust to this factor. Like others, we
measure community environmental pressures using the League of Conservation Voters (LCV) National
Environmental Scorecard, which calculates the proportion of environmental bills favored by each House
of Representatives member. We employ the LCV score of the Representative of the district in which an
establishment is located. We conducted two types of analysis. First, we looked for evidence that
community environmental pressures influenced environmental performance trends. We created a
dichotomous variable called green district, which was coded “1” if the 1994 LCV score of an
establishment’s Representative was greater than the sample median (46 out of 100), and “0” otherwise.
Interacting the moderator with the temporal trend variable, we find no evidence that performance trends
differed based on whether facilities were located in communities with stronger or weaker environmental
preferences (Column 9 of Table A-2 in the Appendix). We then assessed whether controlling for
community environmental preferences affected our primary results by including as an additional control
variable the annual LCV score of an establishment’s Representative. These models, which also control for
historical release trends, yield substantively the same results as our primary model. Together, these results
indicate that our results are robust to considerations of community environmental pressure.
OLS regression
As an alternative to interval regression, we re-estimated our models using ordinary least squares
(OLS) with establishment-level fixed effects, an approach used by others to estimate toxic chemical
emissions from the TRI database (e.g., Hanna and Oliva, 2010; King and Lenox, 2000; Russo, 2009). We
continue to cluster standard errors by establishment. The OLS results (see Table A-3 in the Appendix)
28
support Hypotheses 1, 2, 5,5 and 6b, just as our primary interval regression models (Table 3) do.6 OLS
models continue to support these hypotheses when we also control for historical releases trend (see Table
A-4).
DISCUSSION
Our analysis reveals that organizational characteristics influence the degree to which
establishments improve their performance in response to mandatory information disclosure requirements.
Below, we describe how our study contributes to the literatures on information disclosure and institutional
theory.
Contributions to information disclosure research
Mandating information disclosure as a means of regulating organizational behavior has become
more prevalent in recent years, yet the circumstances under which these programs are successful in
changing organizations’ actions are only beginning to be understood (Fung et al., 2007; Toffel and Short,
2011). The need for evaluation is especially great in the field of environmental policy, in which
information disclosure is especially prevalent and has been referred to as the “third wave” of policy
instruments, following earlier eras of command-and-control (e.g., technology mandates) and marketbased mechanisms such as tradable permits (Delmas, Montes-Sancho, and Shimshack, 2010; Tietenberg,
1998). In this paper, we identify several key organizational attributes that moderate the effect of
information disclosure mandates on organizational performance. We obtain four significant results. First,
establishments located near their headquarters improve more rapidly than other establishments. Second,
5
In testing Hypothesis 5, our OLS models, like our primary results based on interval regression, yield point
estimates that indicate that larger establishments exhibited worse performance trends than smaller establishments
within sparse regions (Column 6 of Tables A-2 and A-3), but better performance trends than smaller establishments
within dense regions (Column 7 of Tables A-2 and A-3), and that the difference between these two effects is
statistically significant (Table A-2: F=14.13, p<0.01, Table A-3: F=13.50, p<0.01). However, whereas the interval
regression models revealed a statistically significant difference in trends within sparse but not dense regions, our
OLS models revealed statistically significant different performance trends within dense regions but only marginally
significant differences in sparse regions. Thus, while interval regression and OLS regression yield robust support for
H5, the different inferences within subsamples suggests caution when interpreting the decomposition between
contexts.
6
The OLS results in Table A-3 also provides supporting evidence of Hypothesis 3 (Column 3: β = -0.031, p < 0.01).
29
establishments located proximate to corporate siblings improve more rapidly. Third, larger establishments
improve more rapidly in dense regions than in sparse regions. Finally, establishments that belong to
publicly traded firms improve more slowly than those owned by privately held firms.
The findings related to proximity to headquarters and corporate siblings suggest that
establishments are particularly careful to protect their images close to their homes and clustered
operations. Prior research has argued that firms are embedded in their headquarters’ communities and
hence are more concerned with their reputations within those communities (Audia and Ryder, 2010;
Marquis and Battilana, 2009). Our results are consistent with this, and further suggest that embeddedness
can occur with respect not only to headquarters but also to clusters of establishments within a region.
We note that our result regarding headquarters proximity contradicts recent research in sociology.
Grant, Jones, and Trautner (2004) find no difference in pollution rates between establishments that are in
the same state as their headquarters and those that are not. Their analysis, however, did not consider
improvement over time, but rather absolute levels of emissions, and the use of a broader geographic
region might mute the effects of both community embeddedness and transfer of capabilities.
An alternative explanation for the proximity results is that proximity enhances capability transfer
(Berchicci et al., 2011), and establishments proximate to headquarters or corporate siblings are thus better
positioned to receive capabilities needed to improve performance. We do not find, however, superior
improvement among establishments that have siblings with above-average historical performance, even if
those siblings are proximate. It thus appears that the effect of proximity on improvement is due more to
increased sensitivity to pressures exerted by the community in which an establishment is especially
embedded than to preferential access to capabilities that can be transferred to the establishment.
Our results with respect to establishment size suggest that the density of local manufacturing
establishments moderates the effect of size on improvement. In relatively dense areas, the visibility of
large establishments appears to outweigh the power they possess, whereas in sparse areas they appear to
be more likely to exercise their power. Our findings extend findings in sociology regarding the sizeenvironmental performance relationship that suggest that large establishments have the ability to abuse
30
their powerful positions within society including the ability to create disproportionate levels of pollution
(Freudenberg, 2005; Grant et al., 2002).
Finally, to our knowledge, ours is the first study that compares the environmental performance of
establishments owned by publicly traded companies to that of establishments owned by privately held
companies. We find that establishments owned by public firms improve significantly more slowly than
establishments owned by privately held companies. Future research is required to distinguish the
mechanisms that underlie these results. Greater pressure on establishments owned by publicly traded
companies to achieve growth (Mascarenhas, 1989) might deter investments in environmental
improvement projects with less certain returns or longer payback periods. In contrast, establishments
owned by privately held firms, which generally have more concentrated ownership, might be more
willing and able to undertake projects that create other forms of utility valued by their owners, which
Berrone et al. (2010) refer to as “socioemotional wealth.” These explanations are not mutually exclusive,
as each assumes a greater tendency of establishments owned by privately held firms to make investments
in environmental improvements even if the financial returns are unclear. Future work is needed to more
precisely identify the mechanisms at play and circumstances under which each type of ownership is more
likely to foster better environmental performance.
Our analysis provides important evidence that organizational features moderate the degree to
which mandatory information disclosure influences subsequent performance. Future research could build
on our results by exploring other organizational characteristics and institutional features upon which
performance improvement is contingent as well as examine other information disclosure contexts that
might provide data on additional dimensions of performance. For example, our establishment-level data
prevents us from exploring how financial performance, brand equity, and board membership might
influence organizations’ responses to information disclosure mandates.
31
Contributions to institutional theory
Institutional theory is particularly well suited to illuminating how external pressures affect
organizations. Of particular importance to strategic management scholarship is the need to further refine
institutional theory to further our understanding of why firms respond differently to common institutional
pressures. For example, why, among firms in similar institutional environments, do some implement
environmental management strategies that go well beyond regulatory compliance requirements while
others pursue more laggard approaches (Delmas and Toffel, 2011)? In particular, what organizational
attributes moderate the effects of institutional pressures?
Our findings suggest that institutional pressures are particularly effective in shaping the behavior
of organizations that possess particular characteristics and capabilities. For example, two organizational
features appear to magnify institutional pressures to improve: proximity to siblings or headquarters, and
being owned by a privately held firm. Our analysis confirmed that both features are associated with
superior performance trends following mandatory information disclosure. Our finding that locallyheadquartered firms are more strongly influenced by communities than by subsidiary units provides
important evidence of a phenomenon that has not received a great deal of prior attention (Marquis and
Battalina, 2009: 296). Our work extends prior research that demonstrated that firms are embedded in the
cities in which they are headquartered, and that this embeddedness has implications for their actions.
Lounsbury (2007), for example, demonstrates that mutual funds’ strategies reflect the logics of the cities
in which they are headquartered, Boston funds being more conservative than those headquartered in New
York. Greenwood et al. (2010) find more geographically concentrated firms, which are more closely tied
to their region’s interests, to be less likely to pursue downsizing strategies. Our findings go beyond prior
studies by explicitly contrasting responses to institutional pressures by establishments proximate to their
headquarters with those of establishments more distant from their headquarters. Our results suggest that
institutional pressures are intensified by internal pressure to improve performance, especially when the
headquarters has a direct interest in avoiding fracturing its (and the establishment’s) relationship with its
home community.
32
There is little prior research on how institutional pressures might affect establishments owned by
publicly traded companies differently from establishments owned by privately held companies, but our
results are somewhat surprising in light of research in a related area. Darnall and Edwards (2006),
investigating the relative implementation costs of environmental management systems (EMS) for
privately and publicly owned firms, found publicly owned firms to have significantly lower
implementation costs, which they attribute to such firms’ superior access to resources. If the costs of
implementing EMS are lower for publicly owned firms, one might expect the costs of acquiescing to
pressure to improve environmental performance to also be lower for such firms, but we find
establishments owned by privately held firms to be more likely to improve under pressure. Our findings
suggest that, at least in our context, privately held firms’ greater resource constraints are outweighed by
their ability to make investments without regard to short-term financial market reactions.
Our findings with regard to establishment size helps resolve the seemingly contradictory
arguments posited in prior research about whether larger or smaller organizations are more responsive to
institutional pressure. Whereas some research argues that larger organizations must be more responsive to
pressure to maintain legitimacy (Goodstein, 1994), other research emphasizes larger organizations’
greater power, which better enables them to resist such pressure (Grant et al., 2002), implying less
responsiveness. That our findings yield evidence that supports both arguments reveals the importance of
accounting for an organization’s context to an understanding of the effect of size. Our finding that, in
regions with many establishments, larger organizations improve faster than smaller ones suggests that the
formers’ need to preserve legitimacy trumps their power over local regulators and pressure groups, which
is diluted by a high concentration of establishments. However, our finding that, in regions with few other
establishments, larger establishments tend to improve less quickly than smaller ones suggests that in this
context the formers’ power to resist local pressure trumps the need for legitimacy.
Our study also reveals the importance to future research in institutional theory of accounting for
both inter- and intra-organizational heterogeneity in attempts to understand the effects of institutional
pressure. That is, we find differences in how establishments reacted to institutional pressures to be based
33
not only on organizational attributes like public versus private ownership, but also on intra-organizational
structures such as proximity to other establishments within the same firm.
In addressing the “lack of understanding of the conditions under which institutional pressures and
organizational characteristics explain the adoption of beyond compliance strategies” (Delmas and Toffel,
2011), our research goes beyond assessing their impact on environmental management practices (e.g.,
Delmas and Montiel, 2009; Delmas and Toffel, 2008; Hoffman, 2001; King et al., 2005) to consider
environmental performance implications. Nonetheless, many opportunities remain for exploring how
institutional pressures and organizational characteristics interact to affect operational and financial
performance indicators in other domains.
Implications for policymakers and managers
Our findings have implications for both the design of information disclosure programs and the
managers of firms targeted by such programs. For policymakers designing information disclosure
programs, our results suggest that their effectiveness depends in part on the industrial organization of a
program’s target population. For example, industries largely populated by establishments owned by
privately held firms may be more responsive to information disclosure programs than industries populated
by establishments owned by publicly traded firms. Understanding and anticipating these differences can
help regulators improve the efficiency and effectiveness of targeting schemes that supplement information
disclosure so as to exert more pressure on laggards.
For corporate managers, our results suggest the need to be aware of how organizational features
are likely to affect subsidiary establishments’ responsiveness to information disclosure programs, which
might in turn affect corporate action. For example, our finding that information disclosure prompts greater
performance improvement among establishments closer to headquarters might lead some corporate
managers to provide more support for (and/or exert more pressure on) more remote establishments.
34
CONCLUSION
Organizations exhibit heterogeneous responses to institutional pressure created by information
disclosure programs. Our analysis of changes in establishments’ environmental performance following
expansion of the EPA’s TRI program suggests that organizational characteristics explain some of this
heterogeneity. With information disclosure programs proliferating, our findings regarding factors that
magnify or dampen their effectiveness in spurring establishments to improve their performance become
more salient with respect to the achievement of these programs’ objectives. In identifying establishmentlevel as well as intra-organizational attributes associated with heterogeneous responses to institutional
pressure, we contribute to an important literature that seeks to leverage institutional theory to explain
heterogeneous organizational strategies.
REFERENCES
Aravind D, Christmann P. 2011. Decoupling of standard implementation from certification: does quality
of ISO 14001 implementation affect facilities’ environmental performance? Business Ethics
Quarterly 21(1): 73-102.
Argote L, McEvily B, Reagans R. 2003. Managing knowledge in organizations: an integrative framework
and review of emerging themes. Management Science, 49(4): 571-582.
Audia PG, Rider CI. 2010. Close, but not the same: locally headquartered organizations and
agglomeration economies in a declining industry. Research Policy 39(3): 360-374.
Basker E, Noel M. 2009. The evolving food chain: competitive effects of Wal-Mart's entry into the
supermarket industry. Journal of Economics & Management Strategy 18(4): 977-1009.
Bennear LS. 2007. Are management-based regulations effective? Evidence from state pollution
prevention programs. Journal of Policy Analysis & Management 26(2): 327-348.
Bennear LS. 2008. What do we really know? The effect of reporting thresholds on inferences using
environmental right-to-know data. Regulation & Governance 2(3): 293-315.
Bennear LS, Olmstead SM. 2008. The impacts of the “right to know”: information disclosure and the
violation of drinking water standards. Journal of Environmental Economics and Management 56(2):
117-130.
Berchicci L, Dowell G, King A. 2011. Where do environmental capabilities reside? Firms’ effects on
their facilities’ environmental performance. Working paper, S. C. Johnson Graduate School of
Management, Cornell University, Ithaca, NY.
Berrone P, Cruz C, Gomez-Mejia LR, Larraza-Kintana M. 2010. Socioemotional wealth and corporate
responses to institutional pressures: do family-controlled firms pollute less? Administrative Science
Quarterly 55: 82-113.
Berrone P, Gomez-Mejia LR. 2009. Environmental performance and executive compensation: an
integrated agency-institutional perspective. Academy of Management Journal 52(1): 103-112.
Blackman A, Afsah S, Ratunanda D. 2004. How do public disclosure pollution control programs work?
Evidence from Indonesia. Human Ecology Review 11(3): 235-246.
35
Boal WM, Ransom MR. 1997. Monopsony in the labor market. Journal of Economic Literature 35(1):
86-112.
Bollinger B, Leslie P, Sorensen A. 2011. Calorie posting in chain restaurants. American Economic
Journal: Economic Policy 3(1): 91-128.
Bresman H, Birkinshaw J, Nobel R. 1999. Knowledge transfer in international acquisitions. Journal of
International Business Studies, 30(3): 439-462.
Carney M, Gedajlovi E. 1991. Vertical integration in franchise systems: agency theory and resource
explanations. Strategic Management Journal, 12(8): 607-629.
Chatterji AK, Toffel MW. 2010. How firms respond to being rated. Strategic Management Journal 31(9):
917-945.
Chew WB, Bresnahan TF, Clark KB. 1990. Learning in a multi-plant network. In Measures for
Manufacturing Excellence, Kaplan RS (ed). Harvard Business School Press: Cambridge, MA.
Christmann P. 2000. Effects of “best practices” of environmental management on cost advantage: the role
of complementary assets. Academy of Management Journal 43(4): 663-680.
Costa DL, Kahn ME. 2000. Power couples: changes in the locational choice of the college educated,
1940-1990. Quarterly Journal of Economics 115(4): 1287-1315.
Currie J, Hanushek EA, Kahn EM, Neidell M, Rivkin SG. 2009. Does pollution increase school
absences? Review of Economics & Statistics 91(4): 682-694.
Cutler DM, Huckman RS, Landrum MB. 2004. The role of information in medical markets: an analysis of
publicly reported outcomes in cardiac surgery. American Economic Review 94(2): 342-346.
Darnall N. 2009. Regulatory stringency, green production offsets, and organizations’ financial
performance. Public Administration Review May/June: 418-434.
Darnall N, Edwards D. 2006. Predicting the cost of environmental management system adoption: the role
of capabilities, resources and ownership structure. Strategic Management Journal 27(4): 301-320.
Darr ED, Argote L, Epple D. 1995. The acquisition, transfer, and depreciation of knowledge in service
organizations: productivity in franchises. Management Science, 41(11): 1750-1762.
David P, Bloom M, Hillman AJ. 2007. Investor activism, managerial responsiveness and corporate social
performance. Strategic Management Journal 28(1): 91-100.
Delmas MA, Montiel I. 2008. The diffusion of voluntary international management standards:
Responsible Care, ISO 9000, and ISO 14001 in the chemical industry. The Policy Studies Journal
36(1): 65-93.
Delmas MA, Montiel I. 2009. Greening the supply chain: when are customer pressures effective? Journal
of Economics & Management Strategy 18(1): 171-201.
Delmas MA, Toffel MW. 2008. Organizational responses to environmental demands: opening the black
box. Strategic Management Journal 29(10): 1027-1055.
Delmas MA, Toffel MW. 2011. Institutional pressures and organizational characteristics: implications for
environmental strategy. In The Oxford Handbook of Business and the Environment, Bansal P,
Hoffman AJ (eds). Oxford University Press.
Delmas MA, Montes-Sancho M, Shimshack J. 2010. Mandatory information disclosure policies: evidence
from the electric industry. Economic Inquiry 48(2): 483-498.
Diestre L, Rajagopalan N. 2011. An environmental perspective on diversification: the effects of chemical
relatedness and regulatory sanctions. Academy of Management Journal 54(1): 97-115.
Dooley RS, Fryxell GE. 1999. Are conglomerates less environmentally responsible? An empirical
examination of diversification strategy and subsidiary pollution in the U.S. chemical industry.
Journal of Business Ethics 21(1): 1-14.
Drope JM, Hansen WL. 2006. Does firm size matter? Analyzing business lobbying in the United States.
Business and Politics 8(2): 1-17.
Edelman LB. 1992. Legal ambiguity and symbolic structures: organizational mediation of civil rights law.
American Journal of Sociology 97(6): 1531-1576.
Elsbach KD, Kramer RM. 1996. Members’ responses to organizational identity threats: encountering and
countering the Business Week rankings. Administrative Science Quarterly 41(3): 442-476.
36
Espeland WN, Sauder M. 2007. Rankings and reactivity: how public measures recreate social worlds.
American Journal of Sociology 113(1): 1-40.
Feldman SJ, Soyka PA, Ameer P. 1997. Does improving a firm’s environmental management system and
environmental performance result in a higher stock price? Journal of Investing 6(4): 87-97.
Fischer HM, Pollock T. G. 2004. Effects of social capital and power on surviving transformational
change: The case of initial public offerings. Academy of Management Journal, 47(4): 463-481.
Florida R, Davison D. 2001. Gaining from green management: environmental management systems
inside and outside the factory. California Management Review 43(3): 64-84.
Freudenberg WR. 2005. Privileged Access, privileged accounts: toward a socially structured theory of
resources and discourses. Social Forces, 84: 89-114.
Fung A, Graham M, Weil D. 2007. Full disclosure: The Perils and Promise of Transparency. Cambridge
University Press: Cambridge, MA.
Gillan SL, Starks LT. 2000. Corporate governance proposals and shareholder activism: the role of
institutional investors. Journal of Financial Economics 57: 275-305.
Goodrick E, Salancik GR. 1996. Organizational discretion in responding to institutional practices:
hospitals and Cesarean births. Administrative Science Quarterly 41(1): 1-28.
Goodstein JD. 1994. Institutional pressures and strategic responsiveness: employer involvement in workfamily issues. Academy of Management Journal 37(2): 350-382.
Grant DS, Bergesen AJ, Jones AW. 2002. Organizational size and pollution: the case of the U.S. chemical
industry. American Sociological Review 67(3): 389-407.
Grant DS, Jones AW, Trautner MN. 2004. Do facilities with distant headquarters pollute more? How
civic engagement conditions the environmental performance of absentee managed plants. Social
Forces 83(1): 189-214.
Grant RM. 1996. Toward a knowledge-based theory of the firm. Strategic Management Journal, 17: 109122.
Greene WH. 2008. Econometric Analysis (6th ed.). Pearson/Prentice Hall: Upper Saddle River, NJ.
Greenstone M, Oyer P, Vissing-Jorgensen A. 2006. Mandated disclosure, stock returns, and the 1964
Securities Acts Amendments. Quarterly Journal of Economics 121(2): 399-460.
Greenwood R, Diaz AM, Li SX, Lorente JC. 2010. The Multiplicity of Institutional Logics and the
Heterogeneity of Organizational Responses. Organization Science. 21 521-539.
Gruber J. 1994. The incidence of mandated maternity benefits. American Economic Review 84(3): 622641.
Halperin NA, Rigotti AC. 2003. US public universities’ compliance with recommended tobacco-control
policies. Journal of American College Health 51: 181-188.
Hamilton JT. 1995. Pollution as news: media and stock market reactions to the toxics release inventory
data. Journal of Environmental Economics and Management 28(1): 98-113.
Hamilton JT. 1999. Exercising property rights to pollute: do cancer risks and politics affect plant emission
reductions? Journal of Risk and Uncertainty 18(2): 105-124.
Hanna RN, Oliva P. 2010. The impact of inspections on plant-level air emissions. The B.E. Journal of
Economic Analysis & Policy 10(1) (Contributions), Article 19. Available at:
http://www.bepress.com/bejeap/vol10/iss1/art19.
Hannan EL, Kilburn Jr. H, Racz M, Shields E, Chassin MR. 1994. Improving the outcomes of coronary
artery bypass surgery in New York State. JAMA: The Journal of the American Medical Association
271(10): 761-766.
Hansen WL, Mitchell NJ, Drope JM. 2005. The logic of private and collective action. American Journal
of Political Science 49(1): 150-167.
Hart SL. 2010. Capitalism at the Crossroads: Next Generation Business Strategies for a Post-Crisis
World (3rd ed.). Upper Saddle River, NJ: Pearson Education.
Henriques I, Sadorsky P. 1996. The determinants of an environmentally responsive firm: an empirical
approach. Journal of Environmental Economics & Management 30(3): 381-395.
37
Hoffman AJ. 2001. Linking organizational and field-level analyses: the diffusion of corporate
environmental practice. Organization & Environment 14(2): 133-156.
Ingram P, Simons T. 1995. Institutional and resource dependence determinants of responsiveness to work
family issues. Academy of Management Journal 38(5): 1466-1482.
Jin GZ, Leslie P. 2003. The effect of information on product quality: evidence from restaurant hygiene
grade cards. Quarterly Journal of Economics 118(2): 409-451.
Jin GZ, Leslie P. 2009. Reputational incentives for restaurant hygiene. American Economic Journal:
Microeconomics 1(1): 237-267.
Kalnins A, Lafontaine F. 2010. Distance to headquarters matters for survival and performance of service
and retail businesses. Working paper, Cornell University, Ithaca, NY.
Khanna M, Anton WQ. 2002. Corporate environmental management: regulatory and market-based
pressures. Land Economics 78(4).
King AA, Lenox MJ. 2000. Industry self-regulation without sanctions: the chemical industry’s
Responsible Care program. Academy of Management Journal 43(4): 698-716.
King AA, Lenox MJ. 2002. Exploring the locus of profitable pollution reduction. Management Science
48(2): 289-299.
King AA, Lenox MJ, Terlaak A. 2005. The strategic use of decentralized institutions: exploring
certification with the ISO 14001 management standard. Academy of Management Journal 48(6):
1091-1106.
King AA, Shaver JM. 2001. Are aliens green? Assessing foreign establishments’ environmental conduct
in the United States. Strategic Management Journal 22(11): 1069-1085.
Konar S, Cohen MA. 1997. Information as regulation: the effect of community right to know laws on
toxic emissions. Journal of Environmental Economics and Management 32(1): 109-124.
Konar S, Cohen MA. 2001. Does the market value environmental performance? Review of Economics
and Statistics 83(2): 281-289.
Lafontaine F, Slade M. 2007. Vertical Integration and Firm Boundaries: The Evidence. Journal of
Economic Literature, 45(3): 629-685.
Lenox M, King A. 2004. Prospects for developing absorptive capacity through internal information
provision. Strategic Management Journal 25(4): 331-345.
Lounsbury M. 2007. A tale of two cities: competing logics and practice variation in the professionalizing
of mutual funds. Academy of Management Journal 50: 289-307.
Maddala GS. 1977. Econometrics. McGraw-Hill: New York.
Marquis C, Battilana J. 2009. Acting globally but thinking locally? The enduring influence of local
communities on organizations. Research in Organizational Behavior 29: 283-302.
Mascarenhas B. 1989. Domains of state-owned, privately held, and publicly traded firms in international
competition. Administrative Science Quarterly 34(4): 582-597.
McConnell VD, Schwab RM. 1990. The impact of environmental regulation on industry location
decisions: the motor vehicle industry. Land Economic 66(1): 67-81.
Moskowitz TJ, Vissing-Jørgensen A. 2002. The returns to entrepreneurial investment: a private equity
premium puzzle? The American Economic Review 92(4): 745-778.
O’Dell C, Grayson CJ. 1998. If only we knew what we know: identification and transfer of best practices.
California Management Review 40: 153-174.
Oberholzer-Gee F, Mitsunari M. 2006. Information regulation: do the victims of externalities pay
attention? Journal of Regulatory Economics 30(2): 141-158.
Okhmatovskiy I, David R. 2011. Setting your own standards: internal corporate governance codes as a
response to institutional pressure. Organization Science, forthcoming.
Oliver C. 1991. Strategic responses to institutional processes. Academy of Management Review 16(1):
145-179.
Peterson ED, DeLong ER, Jollis JG, Muhlbaier LH, Mark DB. 1998. The effects of New York’s bypass
surgery provider profiling on access to care and patient outcomes in the elderly. Journal of the
American College of Cardiology 32(4): 993.
38
Pfeffer J, Salancik, G. 1978. The External Control of Organizations: a resource dependence perspective.
Stanford, CA: Stanford University Press.
Pierce JL, Toffel MW. 2011. The role of organizational scope and governance in strengthening private
monitoring. Working paper, Harvard Business School, Boston, MA.
Potoski M, Prakash A. 2005. Covenants with weak swords: ISO 14001 and facilities’ environmental
performance. Journal of Policy Analysis & Management 24(4): 745-769.
Reid EM, Toffel MW. 2009. Responding to public and private politics: corporate disclosure of climate
change strategies. Strategic Management Journal 30(11): 1157-1178.
Russo MV. 2009. Explaining the impact of ISO 14001 on emission performance: a dynamic capabilities
perspective on process and learning. Business Strategy and the Environment 18: 307-319.
Russo MV, Harrison NS. 2005. Organizational design and environmental performance: clues from the
electronics industry. Academy of Management Journal 48(4): 582-593.
Schuler DA. 1996. Corporate political strategy and foreign competition: the case of the steel industry.
Academy of Management Journal 39(3): 720-737.
Schulze WS, Lubatkin MH, Dino RN, Buchholtz AK. 2001. Agency relationships in family firms: theory
and evidence. Organization Science 12(2): 99-116.
Scorse J. 2010. Does being a “top 10” worst polluter affect facility environmental releases? Evidence
from the U.S. Toxic Release Inventory. Working paper, Monterey Institute of International Studies,
Monterey, CA.
Sine WD, Lee B. 2009. Tilting at windmills? The environmental movement and the emergence of the US
wind energy sector. Administrative Science Quarterly 54: 123-155.
Szulanski G. 1996. Exploring internal stickiness: impediments to the transfer of best practice within the
firm. Strategic Management Journal 17: 27-43.
Szulanski G, Cappetta R, Jensen R. J. 2004. When and how trustworthiness matters: Knowledge transfer
and the moderating effect of causal ambiguity. Organization Science, 15(5): 600-613.
Terlaak A, King AA. 2006. The effect of certification with the ISO 9000 Quality Management Standard:
A signaling approach. Journal of Economic Behavior & Organization 60(4): 579-602.
Tietenberg T. 1998. Disclosure strategies for pollution control. Environmental and Resource Economics
11(3-4): 587-602.
Tolbert PS, Zucker LG. 1983. Institutional sources of change in the formal structure of organizations: the
diffusion of civil service reform, 1880-1935. Administrative Science Quarterly 28: 22-39.
Toffel MW, Marshall JD. 2004. Improving environmental performance assessment: a comparative
analysis of weighting methods used to evaluate chemical release inventories. Journal of Industrial
Ecology 8(1-2): 143-172.
Toffel MW, Short JL. 2011. Coming clean and cleaning up: does voluntary self-reporting indicate
effective self-policing? Journal of Law and Economics. Forthcoming.
U.S. Environmental Protection Agency (EPA). 1994. Addition of certain chemicals; toxic chemical
release reporting; community right-to-know; final rule. Federal Register 29(229): 61432-61502.
U.S. Environmental Protection Agency (EPA). 2004. The toxics release inventory (TRI) and factors to
consider when using TRI data. http://www.epa.gov/tri/triprogram/FactorsToConPDF.pdf, last
accessed January 2011.
U.S. Environmental Protection Agency (EPA). 2010. Risk-Screening Environmental Indicators (RSEI).
http://www.epa.gov/opptintr/rsei/, last accessed January 2011.
U.S.
Environmental
Protection
Agency
(EPA).
2011.
TRI
chemical
list.
http://www.epa.gov/tri/trichemicals/index.htm, last accessed January 2011.
Weil D, Fung A, Graham M, Fagotto E. 2006. The effectiveness of regulatory disclosure. Journal of
Policy Analysis and Management 26(1): 155-181.
Westphal JD, Zajac EJ. 2001. Explaining institutional decoupling: the case of stock repurchase programs.
Administrative Science Quarterly 46: 202–228.
Williams C. 2007. Transfer in context: replication and adaptation in knowledge transfer relationships.
Strategic Management Journal, 28(9): 867-889.
39
40
Table 1. Sample description
SIC
17
20
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
42
49
50
51
52
55
59
73
76
87
Industry
Construction special trade contractors
Food and kindred products
Textile mill products
Apparel and other finished products made from fabrics and similar
materials
Lumber and wood products, except furniture
Furniture and fixtures
Paper and allied products
Printing, publishing, and allied industries
Chemicals and allied products
Petroleum refining and related industries
Rubber and miscellaneous plastics products
Leather and leather products
Stone, clay, glass, and concrete products
Primary metal industries
Fabricated metal products, except machinery and transportation
equipment
Industrial and commercial machinery and computer equipment
Electronic and other electrical equipment and components, except
computer equipment
Transportation equipment
Measuring, analyzing, and controlling instruments; photographic,
medical and optical goods; watches and clocks
Miscellaneous manufacturing industries
Motor freight transportation and warehousing
Electric, gas, and sanitary services
Wholesale Trade-durable Goods
Wholesale Trade-non-durable Goods
Building materials, hardware, garden supply, and mobile home dealers
Automotive dealers and gasoline service stations
Miscellaneous retail
Business services
Miscellaneous repair services
Engineering, accounting, research, management, and related services
Other
Total
41
Establishments
216
2,809
694
107
Percent
0.6%
7.4%
1.8%
0.3%
1,205
820
1,032
553
4,700
503
2,163
135
1,488
2,648
4,359
3.2%
2.2%
2.7%
1.5%
12.3%
1.3%
5.7%
0.4%
3.9%
6.9%
11.4%
2,553
2,593
6.7%
6.8%
1,917
875
5.0%
2.3%
609
299
668
973
1,536
125
279
130
412
111
315
1,348
38,175
1.6%
0.8%
1.8%
2.6%
4.0%
0.3%
0.7%
0.3%
1.1%
0.3%
0.8%
3.5%
100%
Table 2. Descriptive statistics
Panel A. Summary Statistics
1.
2.
3.
4.
5.
6.
7.
8.
9.
Toxic releases to all media of 1995 added chemicals (log pounds)
Proximate headquarters§
Proximate sibling§
Progressive sibling§
Large facility§
Public ownership§
Relative production ratio (log)
Production ratio interpolated
Employees (log)
Obs
217,575
213,762
217,575
164,215
164,215
159,061
217,575
217,575
217,575
Mean
0.90
0.38
0.11
0.30
0.51
0.29
0.09
0.03
3.67
SD
2.89
0.48
0.32
0.46
0.50
0.45
0.29
0.17
2.12
Min
0.00
0.00
0.00
0.00
0.00
0.00
-1.89
0.00
0.00
6.
Max
17.11
1.00
1.00
1.00
1.00
1.00
3.46
1.00
10.09
Panel B. Pairwise correlations
1.
2.
3.
4.
5.
6.
7.
8.
9.
1.
Toxic releases to all media of 1995 added chemicals (log pounds) 1.00
Proximate headquarters§
-0.08
0.02
Proximate sibling§
Progressive sibling§
0.17
0.09
Large facility§
Public ownership§
0.10
Relative production ratio (log)
0.12
Production ratio interpolated
-0.02
Employees (log)
0.06
2.
3.
4.
5.
1.00
0.00
-0.41
-0.21
-0.41
-0.03
-0.01
-0.08
1.00
0.14
0.07
0.09
0.01
0.00
0.05
1.00
0.24
0.33
0.12
0.03
0.28
1.00
0.26
0.05
0.02
0.73
7.
8.
1.00
0.02 1.00
-0.01 0.08 1.00
0.29 0.06 0.01
Notes: § denotes dummy variable whose value is based on establishment status in 1994 (or, if missing, in 1993), before the policy
change that occurred in 1995.
42
Table 3. Primary interval regression results
Dependent Variable: Total releases
Sample:
Proximate headquarters
H1.
H2.
Proximate headquarters × Annual
Counter
Proximate sibling
Proximate sibling × Annual counter
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Full sample
Have
siblings
Have
siblings
Have
progressive
siblings
Have
siblings
In sparse
cities
In dense
cities
Full sample
-0.172**
[0.060]
0.028**
[0.008]
-0.077
[0.064]
-0.013
[0.009]
-0.038
[0.025]
-0.040**
[0.005]
0.038
[0.059]
-0.035**
[0.013]
0.033
[0.045]
-0.020*
[0.009]
Progressive sibling
H3.
0.001
[0.130]
0.043
[0.047]
0.002
[0.008]
Progressive sibling × Annual counter
Progressive, proximate sibling
0.436*
[0.206]
0.003
[0.028]
0.762*
[0.347]
0.093
[0.072]
H4a. Progressive, proximate sibling ×
Annual counter
Progressive, non-proximate sibling
H4a. Progressive, non-proximate sibling ×
Annual counter
Progressive sibling × Proximate
Headquarters
H4b. Progressive sibling × Proximate
headquarters × Annual counter
Large facility
H5.
0.078
[0.050]
-0.006
[0.009]
-0.205
[0.130]
0.010
[0.025]
Large facility × Annual counter
Public ownership
H6.
0.072** -0.020
[0.006] [0.072]
0.008
[0.038]
0.060**
[0.007]
0.080** 0.034** 0.058** 0.035**
[0.006] [0.006] [0.006] [0.003]
0.146+
[0.078]
0.296**
[0.018]
1.144+
[0.647]
Included
101,388
17,737
0.144+
[0.079]
0.293**
[0.019]
1.135+
[0.647]
Included
99,709
17,445
Public ownership × Annual counter
Annual counter
Log number of siblings (in 1994)
Relative production level
Log employment
Constant
Industry (2-digit SIC) dummies
Observations (facility-years)
Number of facilities
0.088** 0.094**
[0.004] [0.004]
0.039**
[0.011]
0.218** 0.211**
[0.053] [0.070]
0.198** 0.245**
[0.012] [0.016]
1.469*
1.096**
[0.653] [0.398]
Included Included
213,762 137,385
37,537
23,806
0.128
[0.098]
0.466**
[0.032]
-0.141
[0.413]
Included
47,275
8,199
0.139+
[0.082]
0.283**
[0.025]
1.118+
[0.677]
Included
84,435
14,893
0.222**
[0.081]
0.262**
[0.027]
1.554
[1.066]
Included
79,780
14,266
0.181**
[0.058]
0.239**
[0.014]
1.134+
[0.617]
Included
159,061
28,279
Notes: All models estimated with interval regression (left censored). Brackets contain standard errors clustered by establishment; **
p<0.01, * p<0.05, + p<0.10. All samples include establishment-years during 1995-2000. The dependent variable is log (plus 1) of total
releases reported to TRI of the 243 toxic chemicals that were added to the TRI chemical list in 1995. These total releases include those
reported as production waste, transfers offsite, and emissions to air, land, water, and underground injection. All models also include a
dummy to indicate observations for which the relative production level values were interpolated.
43
Figure 1.
Average difference in total releases between larger and smaller establishments (H5)
0.8
0.7
0.6
Total releases (log) 0.5
0.4
0.3
1995
1996
1997
Sparse regions
1998
1999
Dense regions
2000
Note: These lines represent the annual average predicted values of larger establishments minus the annual average
predicted values of smaller establishments and 95% confidence intervals, based on Models 5 (sparse regions
represented by thick line) and 6 (dense regions represented by thin line) in Table 3.
44
APPENDIX
Table A-1. Interval regression including historical releases trend
Dependent Variable: Total releases
Sample:
Proximate headquarters
H1.
Proximate headquarters × Annual counter
(1)
Full sample
0.034
[0.045]
-0.020*
[0.009]
Proximate sibling × Annual counter
0.028
[0.053]
0.028**
[0.010]
Progressive sibling × Annual counter
H4b.
(7)
In dense
cities
-0.180**
[0.060]
0.030**
[0.008]
-0.090
[0.064]
-0.008
[0.008]
Progressive, proximate sibling × Annual counter
Progressive, non-proximate sibling ×
Annual counter
Progressive sibling × Proximate headquarters
-0.189
[0.130]
Progressive sibling × Proximate headquarters ×
Annual counter
0.005
[0.025]
Large facility × Annual counter
Public ownership
H6.
0.039**
[0.009]
-0.117**
[0.024]
0.011*
[0.004]
-0.038
[0.052]
0.053**
[0.010]
-0.032
[0.072]
-0.114**
[0.037]
0.001
[0.006]
-0.424**
[0.105]
0.079**
[0.019]
0.048**
[0.010]
-0.116**
[0.025]
0.012**
[0.004]
-0.057
[0.052]
0.051**
[0.010]
0.031**
[0.007]
-0.099**
[0.024]
0.011*
[0.004]
0.052
[0.045]
0.009
[0.008]
0.048**
[0.008]
-0.085**
[0.023]
0.007
[0.004]
0.012
[0.043]
0.021*
[0.009]
-0.003
[0.038]
0.062**
[0.007]
0.029**
[0.005]
-0.101**
[0.017]
0.010**
[0.003]
0.020
[0.032]
0.016**
[0.006]
0.156*
[0.078]
0.292**
[0.018]
1.179+
[0.647]
Included
101,388
17,737
0.139
[0.098]
0.458**
[0.032]
0.007
[0.416]
Included
47,275
8,199
0.154+
[0.079]
0.289**
[0.019]
1.181+
[0.647]
Included
99,709
17,445
0.146+
[0.082]
0.282**
[0.025]
1.129+
[0.682]
Included
84,435
14,893
0.225**
[0.081]
0.262**
[0.027]
1.551
[1.070]
Included
79,780
14,266
0.186**
[0.058]
0.238**
[0.014]
1.154+
[0.622]
Included
159,061
28,279
Public ownership × Annual counter
Annual counter
Historical releases trend
Historical releases trend × Annual counter
Historical releases trend missing
Historical releases trend missing × Annual counter
0.077**
[0.005]
-0.116**
[0.015]
0.009**
[0.003]
-0.039
[0.029]
0.026**
[0.005]
Log number of siblings (in 1994)
Relative production level
Log employment
Constant
Industry (2-digit SIC) dummies
Observations (facility-years)
Number of facilities
(8)
Full sample
0.057
[0.056]
0.020+
[0.011]
Large facility
H5.
(6)
In sparse
cities
0.429*
[0.205]
0.006
[0.028]
0.788*
[0.345]
0.094
[0.071]
Progressive, non-proximate sibling
H4a.
(5)
Have
siblings
-0.001
[0.129]
Progressive, proximate sibling
H4a.
(4)
Have
progressive
siblings
0.032
[0.059]
-0.028*
[0.013]
Progressive sibling
H3.
(3)
Have
siblings
-0.034
[0.025]
-0.040**
[0.005]
Proximate sibling
H2.
(2)
Have
siblings
0.226**
[0.053]
0.196**
[0.012]
1.511*
[0.655]
Included
213,762
37,537
0.073**
[0.006]
-0.131**
[0.021]
0.011**
[0.004]
-0.082*
[0.041]
0.048**
[0.007]
0.038**
[0.011]
0.224**
[0.070]
0.243**
[0.016]
1.168**
[0.397]
Included
137,385
23,806
Notes: All models estimated with interval regression (left censored). Brackets contain standard errors clustered by establishment; **
p<0.01, * p<0.05, + p<0.10. All samples include establishment-years during 1995-2000. The dependent variable is log (plus 1) of total
releases reported to TRI of the 243 toxic chemicals that were added to the TRI chemical list in 1995. These total releases include those
reported as production waste, transfers, and releases to air, land, water, and underground injection. All models also include a dummy
to indicate observations for which the relative production level values were interpolated.
A-1
Table A-2. Interval regression including district LCV score and historical releases trend
Dependent Variable: Total releases
Sample:
Proximate headquarters
H1.
Proximate headquarters × Annual counter
(1)
Full sample
0.072
[0.046]
-0.020*
[0.009]
Proximate sibling × Annual counter
Progressive, proximate sibling
Progressive, proximate sibling ×
Annual counter
Progressive, non-proximate sibling
H4a.
Progressive, non-proximate sibling ×
Annual counter
Progressive sibling × Proximate
Headquarters
Progressive sibling × Proximate
headquarters × Annual counter
Large facility
H4b.
H5.
(5)
Have
siblings
(6)
In sparse
cities
(7)
In dense
cities
-0.183**
[0.060]
0.030**
[0.008]
-0.091
[0.064]
-0.008
[0.008]
0.056
[0.056]
0.020+
[0.011]
0.415*
[0.205]
0.006
[0.028]
0.821*
[0.345]
0.094
[0.071]
-0.193
[0.130]
0.005
[0.025]
Large facility × Annual counter
Public ownership
H6.
(8)
(9)
Full sample Full sample
0.036
[0.130]
0.026
[0.053]
0.028**
[0.010]
Progressive sibling × Annual counter
H4a.
(4)
Have
progressive
siblings
0.037
[0.059]
-0.029*
[0.013]
Progressive sibling
H3.
(3)
Have
siblings
-0.036
[0.025]
-0.040**
[0.005]
Proximate sibling
H2.
(2)
Have
siblings
0.000
[0.038]
0.062**
[0.007]
Public ownership × Annual counter
Green district
Green district × Annual counter
Annual counter
Historical releases trend
Historical releases trend
× Annual counter
Historical releases trend missing
Historical releases trend missing ×
Annual counter
District LCV score
0.077**
[0.005]
-0.115**
[0.015]
0.009**
[0.003]
-0.030
[0.029]
0.027**
[0.005]
0.001
[0.000]
Log number of siblings (in 1994)
Relative production level
Log employment
Constant
Industry (2-digit SIC) dummies
Observations (facility-years)
Number of facilities
0.228**
[0.053]
0.198**
[0.012]
1.517*
[0.655]
Included
213,762
37,537
0.073**
[0.006]
-0.130**
[0.021]
0.011**
[0.004]
-0.067
[0.041]
0.048**
[0.007]
0.000
[0.000]
0.039**
[0.011]
0.225**
[0.070]
0.245**
[0.016]
1.181**
[0.394]
Included
137,385
23,806
0.039**
[0.009]
-0.117**
[0.024]
0.011*
[0.004]
-0.023
[0.052]
0.054**
[0.010]
0.000
[0.001]
-0.032
[0.072]
-0.114**
[0.037]
0.001
[0.006]
-0.413**
[0.105]
0.079**
[0.019]
-0.000
[0.001]
0.048**
[0.010]
-0.115**
[0.025]
0.012**
[0.004]
-0.041
[0.052]
0.051**
[0.010]
0.000
[0.001]
0.030**
[0.007]
-0.098**
[0.024]
0.011**
[0.004]
0.060
[0.045]
0.009
[0.008]
0.001
[0.001]
0.048**
[0.008]
-0.086**
[0.023]
0.007
[0.004]
0.021
[0.043]
0.022*
[0.009]
-0.000
[0.001]
0.029**
[0.005]
-0.101**
[0.017]
0.010**
[0.003]
0.030
[0.032]
0.016**
[0.006]
0.000
[0.000]
0.157*
[0.078]
0.295**
[0.018]
1.239+
[0.642]
Included
101,388
17,737
0.141
[0.098]
0.460**
[0.032]
-0.031
[0.418]
Included
47,275
8,199
0.155*
[0.079]
0.292**
[0.019]
1.232+
[0.641]
Included
99,709
17,445
0.146+
[0.082]
0.285**
[0.025]
1.125+
[0.683]
Included
84,435
14,893
0.225**
[0.081]
0.263**
[0.027]
1.576
[1.068]
Included
79,780
14,266
0.187**
[0.058]
0.240**
[0.014]
1.173+
[0.623]
Included
159,061
28,279
0.004
[0.025]
0.007
[0.005]
0.069**
[0.004]
0.221**
[0.053]
0.208**
[0.012]
1.411*
[0.651]
Included
218,315
38,338
Notes: All models estimated with interval regression (left censored). Brackets contain standard errors clustered by establishment; **
p<0.01, * p<0.05, + p<0.10. All samples include establishment-years during 1995-2000. The dependent variable is log (plus 1) of total
releases reported to TRI of the 243 toxic chemicals that were added to the TRI chemical list in 1995. These total releases include those
reported as production waste, transfers, and releases to air, land, water, and underground injection. All models also include a dummy
to indicate observations for which the relative production level values were interpolated.
A-2
Table A-3. OLS fixed effects models
Dependent Variable: Total releases
Sample:
H1.
Proximate headquarters × Annual counter
H2.
Proximate sibling × Annual counter
H3.
Progressive sibling × Annual counter
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Full sample
Have
siblings
Have
siblings
Have
progressive
siblings
Have
siblings
In sparse
cities
In dense
cities
Full sample
0.008
[0.008]
-0.037**
[0.009]
-0.039**
[0.005]
-0.046**
[0.013]
-0.019*
[0.009]
-0.031**
[0.008]
H4a. Progressive, proximate sibling ×
Annual counter
H4a. Progressive, non-proximate sibling ×
Annual counter
H4b. Progressive sibling × Proximate
headquarters × Annual counter
H5. Large facility × Annual counter
H6.
-0.040**
[0.009]
0.006
[0.028]
0.118+
[0.070]
0.017
[0.025]
Public ownership × Annual counter
Annual counter
Relative production level
Log employment
Constant
Establishment fixed-effects
Observations (facility-years)
Number of facilities
0.102**
[0.004]
0.235**
[0.034]
0.032**
[0.011]
0.873**
[0.047]
Included
213,762
37,537
0.106**
[0.004]
0.271**
[0.045]
0.024+
[0.013]
1.131**
[0.063]
Included
137,385
23,806
0.109**
[0.006]
0.278**
[0.049]
0.031+
[0.019]
1.090**
[0.090]
Included
101,388
17,737
-0.040
[0.071]
0.264**
[0.062]
0.083**
[0.030]
1.186**
[0.155]
Included
47,275
8,199
0.119**
[0.006]
0.282**
[0.050]
0.030
[0.019]
1.075**
[0.090]
Included
99,709
17,445
0.070**
[0.006]
0.164**
[0.048]
0.047*
[0.020]
0.792**
[0.090]
Included
84,435
14,893
0.094**
[0.006]
0.299**
[0.055]
0.039+
[0.021]
0.831**
[0.093]
Included
79,780
14,266
0.053**
[0.007]
0.059**
[0.003]
0.242**
[0.037]
0.043**
[0.014]
0.811**
[0.064]
Included
159,061
28,279
Notes: All models estimated with OLS regression with establishment fixed effects. Brackets contain standard errors clustered by
establishment; ** p<0.01, * p<0.05, + p<0.10. All samples include establishment-years during 1995-2000. The dependent variable is
log (plus 1) of total releases reported to TRI of the 243 toxic chemicals that were added to the TRI chemical list in 1995. These total
releases include those reported as production waste, transfers, and releases to air, land, water, and underground injection. The main
effects of the hypothesized moderators (e.g., proximate headquarters) are time-invariant at the establishment-level, and thus are
absorbed by the establishment fixed effects. All models also include a dummy to indicate observations for which the relative
production level values were interpolated.
A-3
Table A-4. OLS fixed effects models (including historical releases trend)
Dependent Variable: Total releases
Sample:
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Full sample
Have
siblings
Have
siblings
Have
progressive
siblings
Have
siblings
In sparse
cities
In dense
cities
Full sample
H1.
Proximate headquarters × Annual counter
H2.
Proximate sibling × Annual counter
H3.
Progressive sibling × Annual counter
H4a.
Progressive proximate × Annual counter
H4a.
Progressive distant × Annual counter
H4b.
0.008
[0.025]
H5.
Progressive sibling × Proximate headquarters
× Annual counter
Large facility × Annual counter
H6.
Public ownership × Annual counter
0.072** 0.068** 0.054** -0.053
[0.005] [0.005] [0.009] [0.071]
0.003
0.006
0.008+ 0.001
[0.003] [0.004] [0.004] [0.006]
0.057**
[0.007]
0.064** 0.045** 0.063** 0.033**
[0.009] [0.007] [0.007] [0.005]
0.008+ 0.005
0.001
0.005
[0.004] [0.004] [0.004] [0.003]
0.063**
[0.005]
0.268**
[0.034]
0.032**
[0.011]
0.838**
[0.047]
Included
213,762
37,537
0.080**
[0.010]
0.306**
[0.050]
0.024
[0.018]
1.092**
[0.089]
Included
99,709
17,445
Annual counter
Historical releases trend × Annual counter
Historical releases trend missing × Annual
counter
Relative production level
Log employment
Constant
Establishment fixed-effects
Observations (facility-years)
Number of facilities
-0.041**
[0.005]
-0.033*
[0.013]
-0.018*
[0.009]
0.011
[0.010]
0.003
[0.011]
0.010
[0.028]
0.117+
[0.070]
0.015+
[0.008]
0.079**
[0.007]
0.315**
[0.045]
0.027*
[0.013]
1.081**
[0.063]
Included
137,385
23,806
0.082**
[0.010]
0.302**
[0.049]
0.025
[0.018]
1.106**
[0.089]
Included
101,388
17,737
0.101**
[0.019]
0.274**
[0.062]
0.081**
[0.030]
1.193**
[0.153]
Included
47,275
8,199
0.045**
[0.008]
0.185**
[0.048]
0.045*
[0.020]
0.787**
[0.090]
Included
84,435
14,893
-0.027**
[0.008]
0.057**
[0.008]
0.322**
[0.055]
0.034+
[0.020]
0.831**
[0.092]
Included
79,780
14,266
0.054**
[0.006]
0.267**
[0.037]
0.036**
[0.014]
0.819**
[0.064]
Included
159,061
28,279
Notes: All models estimated with OLS regression with establishment fixed effects. Brackets contain standard errors clustered by
establishment; ** p<0.01, * p<0.05, + p<0.10. All samples include establishment-years during 1995-2000. The dependent variable is
log (plus 1) of total releases reported to TRI of the 243 toxic chemicals that were added to the TRI chemical list in 1995. These total
releases include those reported as production waste, transfers, and releases to air, land, water, and underground injection. The main
effects (e.g., proximate headquarters) are time-invariant at the establishment-level, and thus are absorbed by the establishment fixed
effects. All models also include a dummy to indicate observations for which the relative production level values were interpolated.
A-4
Table A-5. Robustness tests: Alternative definitions of proximate and progressive
Dependent Variable: Total releases
Sample
Proximate headquarters (same 3-digit Zip Code)
H1.
H1.
Proximate headquarters (same 3-digit Zip Code) ×
Annual counter
Proximate headquarters (same state)
(1)
(2)
(3)
(4)
(5)
Full sample
Full sample
Have
siblings
Have
siblings
Have
siblings
-0.040
[0.025]
-0.040**
[0.005]
-0.004
[0.026]
-0.033**
[0.005]
Proximate headquarters (same state) × Annual counter
Proximate sibling (same 3-digit Zip Code)
H2.
H2.
0.080*
[0.040]
-0.021**
[0.008]
Proximate sibling (same 3-digit Zip Code) ×
Annual counter
Proximate sibling (same county)
0.029
[0.041]
-0.012
[0.008]
Proximate sibling (same county) × Annual counter
Progressive industry sibling
H3.
Progressive industry sibling × Annual counter
Annual counter
0.089**
[0.004]
0.090**
[0.004]
0.217**
[0.053]
0.197**
[0.012]
1.480*
[0.656]
Included
213,762
37,537
0.218**
[0.053]
0.201**
[0.012]
1.448*
[0.653]
Included
213,762
37,537
Log number of siblings (in 1994)
Relative production level
Log employment
Constant
Industry (2-digit SIC) dummies
Observations (facility-years)
Number of facilities
0.096**
[0.005]
0.038**
[0.011]
0.210**
[0.070]
0.245**
[0.016]
1.066**
[0.398]
Included
137,385
23,806
0.093**
[0.004]
0.039**
[0.011]
0.211**
[0.070]
0.245**
[0.016]
1.091**
[0.398]
Included
137,385
23,806
0.086+
[0.052]
-0.004
[0.009]
0.074**
[0.005]
0.147+
[0.078]
0.295**
[0.018]
1.153+
[0.646]
Included
101,388
17,737
Notes: All models estimated with interval regression (left censored). Brackets contain standard errors clustered by establishment;
** p<0.01, * p<0.05, + p<0.10. All samples include establishment-years during 1995-2000. The dependent variable is log (plus 1)
of total releases reported to TRI of the 243 toxic chemicals that were added to the TRI chemical list in 1995. These total releases
include those reported as production waste, transfers, and releases to air, land, water, and underground injection. All models also
include a dummy to indicate observations for which the relative production level values were interpolated.
A-5
Table A-6. Robustness tests: Alternative definitions of large establishment as well as sparse and dense regions
Dependent Variable: Total releases
Sample:
Large facility
H5.
Large facility × Annual counter
(1)
(2)
(3)
(4)
In sparse
counties
In dense
counties
In sparse
cities
In dense
cities
-0.241**
[0.063]
0.031**
[0.008]
-0.015
[0.061]
-0.014+
[0.009]
0.044
[0.064]
0.039**
[0.010]
0.040**
[0.005]
0.148+
[0.082]
0.227**
[0.020]
1.224+
[0.683]
Included
84,435
14,893
0.265**
[0.071]
-0.010
[0.011]
0.055**
[0.005]
0.226**
[0.081]
0.186**
[0.022]
1.789+
[1.058]
Included
79,780
14,266
Largest 25th percentile facility
H5.
Largest 25th percentile facility ×Annual counter
Annual counter
0.030**
[0.006]
0.054
[0.081]
0.315**
[0.026]
1.189+
[0.712]
Included
83,110
14,588
Relative production level
Log employment
Constant
Industry (2-digit SIC) dummies
Observations (facility-years)
Number of facilities
0.060**
[0.006]
0.296**
[0.083]
0.226**
[0.025]
1.241+
[0.664]
Included
81,105
14,571
Notes: All models estimated with interval regression (left censored). Brackets contain standard errors clustered by establishment;
** p<0.01, * p<0.05, + p<0.10. All samples include establishment-years during 1995-2000. The dependent variable is log (plus 1)
of total releases reported to TRI of the 243 toxic chemicals that were added to the TRI chemical list in 1995. These total releases
include those reported as production waste, transfers, and releases to air, land, water, and underground injection. All models also
include a dummy to indicate observations for which the relative production level values were interpolated.
The following subsamples were used: Column 1 includes establishments in sparse cities (fewer than the county median of 41
establishments); Column 2 includes establishments in dense cities (greater than the city median of 41 establishments); Column 3
includes establishments in sparse cities (fewer than the city median of 11 establishments); Column 4 includes establishments in
dense cities (greater than the city median of 11 establishments).
A-6